• The benefits of using big data for small businesses
• How your business can embark on its big data journey

Let us get started.

The Big Data Edge For Small Companies

Benefit #1: Minimizes Costs

An efficient business process will help minimize costs. With big data, your small business may receive the information required to reveal operational inefficiencies. That way, your business can resolve issues causing these inefficiencies. For example, your small gift shop may use data to discover that a large portion of your customers are not interested in buying gift wraps whenever they make a purchase. That means your store should stop inventorying gift-wrapping supplies at the checkout. This decision will eventually result in cost reduction at an operational level.

Benefit #2: Maximizes Sales

Small businesses with access to big data can gain massive insights into the buying preferences and shopping beliefs of their customers. Armed with this data, small companies like yours can easily understand how to customize their offerings to give customers what they want. Nothing more, nothing less. A small business that modifies its offerings this way will definitely increase its sales.

Benefit #3: Brings An Advantage

Your investments in big data will help you unlock a world of growth opportunities for your small business. That way, you will easily focus on the preferences of your local customers. The fact is, data tools eliminate the guesswork when it comes to strategizing for your local market. Moreover, customer data will also help small businesses gain actionable insights into the buying behaviors of their customers. And when your small business has a clear idea of how your customers think and what offerings they dislike or like, you will be able to personalize your offerings just for them. And this capability to personalize your offerings will be your business’s biggest competitive edge.

Benefit #4: Improves Decision-Making

Like every other small business, your business must be on social channels – LinkedIn, Facebook, Twitter, Instagram, and the like. And if that is the case, you should think of social media mining – it is performing data mining of your social profiles. When you do this, your business will gather actionable insights into the buying interests, purchase patterns, etc., of your social media users. With these insights, you will be able to create well-targeted marketing campaigns for different segments. Moreover, you have a range of data processing tools that can help your business mine its different social media platforms.

Benefit #5: Enhances Pricing Decisions

A small business that prices its services and products properly can succeed. But nailing your offering’s pricing is easier said than done. Here is where powerful data tools come into play. If your small business invests in data tools, you will easily assess your competitors’ finances and organizational structures. This comprehensive data-backed competitor analysis can help you compare your pricing strategy with your competitors’. With the help of data, a small business can easily determine if it should lower or raise its pricing and even confirm that its prices are aligned with that of its competitors.

Get Started With Data Analytics For Your Small Business

At Flexsin, we can help your business leverage big data effectively. We have deep capabilities in data science to help you make the most of available data assets. From carrying out data analyses to performing data visualization and data interpretation, our team has vast experience in helping businesses make sense of their unexplored data assets. Our data science services include data acquisition, data analyses, and data cleansing. Moreover, we have in-depth capabilities in working with different technologies – AI, data analytics, ML, and the like. Using these technologies, we help businesses gain mission-critical insights into their data lakes. We can help your business gain key insights from vast datasets stored in different databases: Hadoop, MongoDB, CouchDB, MySQL, NoSQL, etc. Connect with our big data specialists today and discover how we can help your business get insights from the available data assets.

Airline industry, especially the commercial aviation sector, is striving to improve how they operate and serve their customers. They need data and verification models to control costs arising from various operational activities.

Airline data can be integrated and analyzed to develop effective forecasting models to assess the impact of various parameters, such as introducing new routes and adjusting fares. Big data analytics can help in automating airline data and activity reports such as estimates for performance management and revenue generated for specific routes or sectors.  Artificial Intelligence (AI) has made it possible to enhance customer experiences with automation, optimization of employee workflow and self-service solutions.

Airlines leveraging big data and artificial intelligence

Leading airlines, airports and travel tech innovators are using big data to deliver seamless customer experience. United Airlines uses a system that analyses 150 variables in a customer’s profile including their previous purchases to provide customized offerings. E-tourism market player FoxTripper is using software to predict the best places to be in for the passengers based on a moving map that collects data about the passengers’ travel behavior. British low-cost carrier EasyJet is leveraging AI to determine seat pricing in real-time, depending upon the demand. Delta Airlines has developed a novel application for baggage tracking predictive aircraft maintenance.

Dynamic pricing for value-added products

Willingness to Pay (WTP) reveals when a customer is likely to pay a maximum price for a service or product. Most passengers are willing to pay a higher price for tickets on the day before departure (DBD). The concept of WTIP is connected to dynamic pricing – the practice of pricing a value-added data product based on the customer’s WTP.

Critically interrelated datasets

Data Management and Interface Management are critical components that need to support and interact with each other. The interface provides data from legacy systems to which business rules can be applied to curate and authenticate data products for analytical use. That way, business rules management, interface management and metadata management are critically interrelated and need to be efficiently integrated for insightful analysis.

Data analytics helps the industry to understand customers’ preferences and other operational issues. Analysis of ticket booking allows to target customers with personalized offers while optimizing price in real-time utilizing predictive analysis techniques.

Data architecture and storage solutions

Below six types of reference data architectures are currently employed by developers that provide a variety of storage solutions to the airline and airport industry:

Relational storage: It uses structured query language (SQL) and is optimized for non-hierarchical data that does not rely on the large collections of binary objects. Relational design is ill-suited for rapidly changing data sources due to the effort required to revise the schema.

Graph Storage: It’s a specialized type of storage for reading and writing graph structures at a high speed. It does not readily support the indexing and retrieval of documents.

Document storage: This commonly used noSQL data store keeps records as discrete documents rather than rows in a table. It tends to be more flexible than relational store as it is optimized to store document collections but it does not support the transactional system well.

Hybrid storage: The data storage architecture combines the features of one or more of other data store types and tends to scale well. However, as it is not optimized to a specific data type, hybrid store will not maximize performance in any given category.

Key-value storage: The data type supports the native storage of documents, records, and large binary objects. It is designed to optimize extremely large datasets but lacks many basic functions and ACID compliance that are standard in the document and relational stores.

Geospatial storage: The data storage architecture allows airlines to utilize geospatial functions in document and relational stores. It combines features to replace master content stores and reduces major costs of deployment and data maintenance by eliminating duplication of efforts.

Additionally, airlines can also utilize Microsoft’s SQL Server Reporting Services or SSRS to prepare and deliver a variety of interactive and printed reports. SSRS provides an interface into Microsoft Visual Studio for SQL administrators and developers to connect to the SQL database and use SSRS reports in a variety of ways. SSRS flexibly delivers the right information to the right user.

SQL Server Data Tools for Business Intelligence (SSDT)

SQL Server Data Tools for Business Intelligence (SSDT) reduce the report definition language component or RDL in a graphical user interface so that instead of writing code, the developer can drag-and-drop graphic icons into an SSRS report format. RDL (Report Definition Language) reports can be viewed with the help of Microsoft SQL Server or ASP.NET ReportViewer web control.

Optimizing the airspace use

With the airport traffic increasing day-by-day, big data analysis enables airlines to optimize airspace use and develop an efficient revenue management model. Sentiment analysis and travel journey analysis can be used to keep the customers updated in real-time, promoting special offers based on their preferences. Big data analytics provides some key benefits to the airlines, such as lower operational costs, market-leading competitiveness, stakeholder value and increased shareholder value.

Data subscribing to SVAULT standards

Flexsin has worked on airline projects in which the client’s datasets were residing in legacy systems with brittle and antiquated architecture that restricted access due to which the client was facing a lot of issues related to insights of their passengers’ information.  Our developers worked on this requirement of the client to make the data Secure, Visible, Accessible, Understandable, Linked and Trusted (SVAULT). Our developers transformed, harmonized and provisioned the data in a way that enabled the extraction of the maximum value for the client.

Driving value for your aviation business

When your business planning pivots around passenger priorities and schedules, you need access to integrated airline data that is accurate and available to you at a frequency you require. There are many areas in airline the industry that can be tapped by big data and AI solutions to understand their customers individually, and also predict the requests that might come up.

With dynamic data feed and access to new market insights, Flexsin Technologies, a business intelligence development company can help businesses with airline-specific insights for global ticketing and sales. Our airline data integration solutions can help you see a more accurate view of your marketing landscape to increase efficiencies.  Let’s help you too drive value for your aviation business with our capability to curate, clean and seamlessly integrate data.

BI and Analytics solutions are helping multitude of businesses, ranging from startups to enterprises, identify and interpret patterns, trends, anomalies and relationships from their in-house based data assets in collaboration with external sources of data. BI solutions have proven to drive enhancements in operational processes and enactments, while empowering employees to generate individual reports, run queries, share insights, collaborate with other and run their own analyses.

The incorporation of BI and Big Data Analytics technology within your organization provides you with influential acumens via Internet or in your hands with a smartphone to make the right just in time. Moreover, it also transforms your company into a data-driven, well-oiled machine. As the adage goes, data does not lie!

The assessment and planning

It starts with the assessment of required BI results, creation of roadmaps, analyzing possible solution landscapes, completion of proof of concepts and lastly, deciding on the right technology and tools.

Implementation and full integration

It starts by choosing the right and full-fledged BI platform design, architecture, which then extends to implementation, incorporation, development, and validation of the entire process for business’ success.

BI DataOps

At this stage, your business is given DataOps’ support in order to drive BI platform with integration of additional data source, performance improvements and continued data quality.

Required technological expertise in BI integration

Speaking of it, we would like to proceed with how we do it – Your business receives full-scale support in order to implement BI and analytics leveraging the benefits of advanced BI platforms, DataOps practices and enterprise data management platforms. Following is a set of BI expertise and experience required in the process:

• Power BI
• Tableau
• Logi Analytics
• Looker

• Always getting it right in collaboration with top-notch BI experts
• Acceleration of success ensuring robust relationships with world’s leading providers of BI technology
• Empowering employees with strong Business Intelligence competences for decision making and eloquent insights.
• Saving company overheads by choosing cost-effective GDM (Global Delivery Model).

Big Data Analytics

A properly carried out Big Data Analytics solutions always help your business incorporate and put large volumes of data together from incongruent sources, transmuting raw data into valued information resources for quicker and prices analytics, automation and decision-making and automation. The right big data analytics solutions will always help your organization drive operational, customer, industry-oriented IoT results through prescriptive, cognitive, diagnostics and predictive analytics.

While the process of big data analytics’ implement and integration are virtually similar, let’s focus on the required set of technology expertise:

It focuses on bridging the gap between massive data generation and businesses demanding for data to leverage and experience impactful insights. You will be given full-scale assistance to implement functional big data analytics solutions based on modern platforms, supportive DataOps practices and enterprise data management platforms. Following this, there comes the requirement for technology expertise and platforms to be chosen accordingly. As far as we speak of our expertise, we have proficiency in working with:

• Azure cortana intelligence suite
• Apache big data platform

• Critical thinking: The experts, who are otherwise called data scientists, should have ability to think critically. They must be able to understand and apply independent analysis of facts on a provided context before developing opinions or giving out judgements.
• Coding: They must know how to write codes and be comfortable managing numbers of programming assignments.
• Proficient in deep learning, machine learning and AI: For every industry is moving in these areas fast, there is a demand for a massive volume of data every now and then. Therefore, your prospective service provider must have deep understanding of the field and potential challenges there, only to resolve them without hampering the entire project.
• Data architecture: It is important for the team to understand what will happen to the data during their transformation from the beginning to model to ultimate business decision.
• Risk analysis: A good Big Data Analytics agency will always understand that the importance of analyzing potential business risk. They will, as a result, will build elaborate analysis on potential risks, only to mitigate them.

Flexsin came a long way throughout its time in the industry, i.e. over a decade. We are a fully self-funded model, growing nearly 500% years to date. Being one of the most trusted and top IT firms in recent markets, we further strive to stand apart ensuring consumer-specific solutions that fit right our clients’ budget.

Oracle has long provided complete data ecosystem to power enterprise data management, and in the current age of big(ger) data and smart data, it has come with entirely new and broad set of infrastructure and platform services to work together in a coherent way. It’s a key player in Xaas (Anything as a Service) market space for Saas (Software as a Service), IaaS (Infrastructure as a Service) and PaaS (Platform as a Service) applications. Oracle cloud offerings provide the benefit of a modern cloud platform that can be effectively used as a foundation for XaaS transformations.

Scalability and agility of workload

SaaS facilitates subscription-based usage of application software in the cloud that teams can use for various business processes depending upon the maturity and complexity of the organization’s IT assets. Oracle’s PaaS offerings such as JCS, IoT, MCS and SOACS allow for the custom development of applications to realize a range of benefits of the XaaS model. IaaS allows the organizations to enhance the scalability and agility of any workload regardless of their databases, operating systems and VMs.

Dual-format architecture

Oracle offers dual-format architecture using row analysis for OLTP (Online Transactional Processing) and in-memory columnar analysis for analytics, allowing the organizations high availability and scalability of databases.

As Oracle embeds algorithms directly onto the microprocessor, they can keep adding more cores and threads, thus speeding up the completion of core tasks such as encryption and compression directly onto the chip.

Massively run parallel Oracle SQL queries

Oracle Big Data Appliance runs on Apache Hadoop and Spark, letting the organizations set up their big data system quickly and relatively at a lower cost of installation. Another Oracle technology viz. Oracle Big Data SQL allows the analysts to easily run massively parallel Oracle SQL queries across Hadoop, NoSQL and relational databases.

Zero Data Loss Recovery Appliance with database aware recovery

Today, businesses need to have data backup happen constantly in real time as they can’t afford to lose data between backups with the traditional batch-oriented, disk-based backups. Oracle has addressed the issue of data loss with its Zero Data Loss Recovery Appliance that prevents not only data loss but also leaves minimum impact on the production servers. This specialized engineered system is able to achieve this as it takes up back up of only the changes done to the database, and doesn’t copy the whole of it. Its innovative database-aware recovery feature carries out validation of data as it backs up, and cloud-scale protection allows a single appliance to back up a whole data center!

Multitenancy for enterprise grade container infrastructure

Enhanced SQL allows developers to use JSON data that replaces XML as the format for data with complex structures such as webpages, to store natively as columns in Oracle database. So, pros can do any relational database task with that data. Oracle also has embedded container technology directly into the relational database with multitenancy. So, there is less software administration burden for an organization’s IT compared to OS based visualization as each virtual machine or pluggable database has a copy of all the software. Oracle Cloud Container Service (OCCS) creates an enterprise grade container infrastructure that can be set up faster so that the teams can focus more on the application and less on the underlying infrastructure.

Growing trend for data sovereignty

As the organizations are realizing data as key asset, there has been an increasing voice for it to be free, not in the monetary sense but in terms of accessibility and ubiquity. Organizations have to expose the data widely for the analysts to create value yet they need to make it accessible, understandable and actionable across business units and geographies. This requires a radical approach to data architecture and governance, leveraging the capabilities of natural language processing and machine learning to make sense out of the data. There has been a growing trend for data sovereignty among the organizations, and hence there has been increased focus at Oracle to deliberately develop technologies for managing, monetizing and unlocking the true value of this increasingly crucial enterprise asset that we call DATA.

Cloud-native, container native and low-code development

Oracle provides offerings for accelerated application development and deployment with its API-first, mobile first cloud applications for cloud-native, container native and low-code development. Oracle Ravello ensures that silo developments do not impact an entire application, allowing the enterprises run exiting VMware workloads on cloud without making any modifications or moving and readdressing individual hosts.

For most Oracle application development environments currently in use, they point towards a hybrid cloud model. Oracle DBAs and managers need to balance the scale and demands of the businesses to deliver high performing and responsive systems.

2018 has been a specifically major yes for the industry of business intelligence, the trends we had presented yesteryear would carry on throughout 2019. The BI landscape, however, is constantly evolving, and the future of BI is played now, with rising trend to focus on. In the year of 2019, BI strategies are supposed to get progressively more customized. Organizations of all sizes are no more questioning if they want enhanced access to BI analytics, however what does make the best Business Intelligence solution for their individual business. Organizations are no longer speculating if data visualization can mend analyses, but what’s the smartest way to tell each data-story. 2019 is going to be the year of data discovery and data quality management: Secure and clean data pooled with an easy-to-understand and solid presentation. 2019 will as well be a year dog multi0-cloud strategies and AI. We are super excited to witness what this New Year has to offer. Get to read our 4 top business intelligence trends for 2019 –

• Data quality Management:

The data quality analytics trends have grown widely in 2018. The emergence and wide acceptance of Business Intelligence to extract and analyze value from the numbers of sources of data that be gather at high scale, got alongside a horde of errors and degraded reports: The inequality of data sources and data kinds had added a little bit of more intricacy to the data integration process.

• Data recovery:

This has maximized its effect over the past year. The already highlighted survey done by the Business Application Center registered data discovery in the top three business intelligence trends by the significance hierarchy. The top practitioners of business intelligence progressively display that the assent of business users is a powerful and consistent trend. Moreover, business users are to require software that is: flexible and agile, easy-to-use, to reduce time to insight and to allow simple handling of a high volume and numbers of data.

• Artificial Intelligence:

This happens to be one of the leading trends picked by Gartner in their 2019 Strategic. Besides, businesses are as well considering combining AI with sovereign things, and focusing on the extent of sophistication AI communicates with its environment. AI is the technology targeting to make machines perform what is typically done by intricate human intelligence. More of than not, it is seen as the greatest foe-friend of our race in movies such The Machines of Matrix or Terminator; Artificial Intelligence is never yet on the verge to destroy the earth, despite the legit cautions of a few renowned tech-entrepreneurs and scientists.

• Connected cloud:

The pervasiveness of the cloud is nothing new to anybody keeps up-to-date with business intelligence trends. In the coming year, the cloud is believed to continue reigning with more and more businesses moving toward cloud connecting as a consequence of the explosion of cloud-based tools available in the market. Furthermore, entrepreneur will get to know about how to adopt the cloud analytics power, where most of the components – data models, data sources, computing power, processing applications, data storage and analytic models – will be stored in the cloud.

So, how about becoming data-driven in 2019?!

To be data-driven is no more an ideal; it’s now an expectation in the new age business market. 2019 is going to be exciting time of looking past all hype and moving toward to excerpt the maximum value from state-of-the-art online reporting software.

However, in areas like social media, there is no specific structure boundary for the data used.  In such situations, it become challenging for RDBMS to provide cost effective Crude, Read, Update and Delete (CRUD) operations as it has to deal with relationships among various data.

Therefore, the need for new mechanism was felt to handle such large amount of unstructured data in an easy and efficient manner by web development company and other stake holders. That way, NoSQL came into picture to deal with unstructured Big Data in efficient way to achieve maximum business value and customer satisfaction.

In this post, we will look at the key differences between relational and NoSQL databases, their limitations and advantages, and the reasons why NoSQL databases are growing in popularity.

Let’s start with key features of RDBMS

Key features of RDBMS

• Structured way of data storage
• Values are atomic
• RDBMS schema provides a logical view of data organization and provides information on how the relations are associated
• Sequence of columns and row is insignificant
• Data storage is vertically scalable
• Integrity constraints maintain data consistency across multiple tables

Limitations of RDBMS

To scale the database, it needs to be distributed on multiple scales
Users have to scale the database on expensive servers that are hard to handle
In case your database doesn’t fit into tables, you will need to design a complex database structure

Reasons to choose RDBMS database

• ACID compliancy (Atomicity, Consistency, Isolation, Durability) is ensured
• Data remains structured and unchanging

Examples of RDBMS include MSSQL, Oracle, Microsoft Azure and IBM DB2.

Now, we will move our discussion to NoSQL database.

NoSQL or Not Only SQL (No Structured Query Language) provides an unstructured way of data storage.

Features of NoSQL

• It’s a collection of key-values pair, documents and wide-column stores without any standard schema definition
• Supports integrated caching
• Distributed computing
• Flexible schema
• Powerful, efficient architecture
• Easily scalable
• No complex relationships, such as the ones between tables and RDBMS
• Low cost servers allow for scaling of more data

Limitations of NoSQL

• Lack of reporting tools for performance testing and analysis
• Not appropriate for complex queries as there is no standard interface to perform queries
• No defined standards for database

Reasons to use a NoSQL

• Large amount of data with little or no structure can be easily stored
• Cloud computing and storage is effectively utilized
• Rapid development

Examples of NoSQL

MongoDB, Apache Cassandra and Hbase

NoSQL is a schema-less alternative to SQL and RDBMS. The database is designed to store, process and analyze extremely large amounts of unstructured data.

Advantages of NoSQL over RDBMS

Elastic Scaling

NoSQL database were created to overcome the limitations of RDBMS. Compared to RDBMS, NoSQL databases are flexible and scalable, and also have superior performance. RDBMS does not scale out easily on commodity clusters while NoSQL can expand transparently to take advantage of new nodes, thus substantially reducing commodity hardware costs.

Sizable Data

While it is becoming impossible for RDBMS to handle tremendously growing databases, NoSQL systems are capable to handle large volumes of data such as Hadoop and Outstrip.

Flexible Data Models

With RDBMS, change management is a real problem as even the smallest changes need to be aptly managed else service levels will be compromised. On the other hand, due to its fewer restrictions, rigidly defined Big Table-based NoSQL database can be easily used for the creation of new columns.

An overview of NoSQL

In a distributed system, managing Consistency (C), Availability (A) and Partition Tolerance (P) or CAP is of vital importance. CAP theorem put forward by Eric Brewer states that if you get a network partition, you have to trade off availability of data for its consistency. Even durability can also be traded off against latency. NoSQL database allows the developers to choose from and fine tune the system to their specific requirements.

The key difference between RDBMS and NoSQL is that while RDBMS is Transaction Consistent, NoSQL is Eventuality Consistent. RDBMS systems require the involvement of expensive DBAs for installation, design and ongoing tuning while NoSQL databases have simpler data models and automatic repair that significantly lower down the maintenance costs.

NoSQL better suited for real time analytics

NoSQL databases, in general, avoid RDBMS functions such as multi-table joins that can result in high latency. NoSQL offers choices of strict to no relaxed consistency that one needs to look on individual case basis. NoSQL setting is better suited to real time analytics and also when data is brought together from any upstream system to build an application. BI-tool support for NoSQL is growing steadily.

For a long time, RDBMS has provided mechanisms to store data persistently, concurrency control and integrating application data. However, its dominance is now receding.

Today, enterprises need to be informed at all times about the multiple opportunities and lurking risks they face in an everyday business environment.

When it comes to data warehousing capabilities, Microsoft Power BI offers unmatched features. This fabulous business intelligence and analytics tool allow enterprises to extract data from multiple disparate sources to derive meaningful insights out of it. With Power BI, managers can view charts and reports for predictive analytics to easily visualize what the future holds for the performance of their organization. With its delighting visualization features, Power BI raises the analytics several notches up.

Customers demand business insights and they want it now. So, one can set priorities to concentrate on the metrics that matter. Personalized dashboards, charts, and reports allow for the use of data that really matters. It allows for changing data dimensions and measures in the fly and coming up with needed outcomes. It provides faster time to delivery and removes unnecessary dependencies on the IT staff.

Power BI is really cool for multiple reasons, like:

• Every data import from SQL serves both on-premises and in the cloud, flat files, spark clusters and what you have.
• Offers the best of both worlds when it comes to simplicity and performance
• Data Analytics Expressions (DAX) scripting for creating measures and columns.
• The cloud-based service is made feature-rich with three tools viz. Power Query, Power Pivot, and Power View
• Charts are interactive and connected by default.
• In-memory analytics and columnar database support tabular data.
• Visualizations look great with little effort.

Natural language processing (NLP) makes it quite easy to query information from Power BI using natural human language. Tasks like data discovery, data preparation, and designing of the interactive dashboards can be harmoniously done with this useful business tool. Dataset in Power BI can be sliced and diced in multiple ways. End users can query the dataset with natural language and have their answers displayed in charts and graphs.

Insightful decision making 

Power BI dashboard provides a comprehensive view for business users with their highly important metrics under one roof, update in real time and accessibility on every device. The dashboard is revolutionary in the sense that it derives insights from the data to further scour them for improved decision making.

Opening up visualization 

We often tend to underestimate the importance of visualization, but it is crucial to impress business people. Power BI seamlessly pulls and integrates diverse databases and disparate formats to gain meaningful insights that businesses need to stay ahead of the competition curve. One can use the Power BI REST API for pushing data from any cloud setup. The tool can pull data from a wide range of cloud services, including QuickBooks, MailChimp, Salesforce, Zendesk, GitHub, Twilio and even Google Analytics.

Why Power BI when we already have the tried and tested MS Excel 

Excel for long has been Microsoft’s presentation layer for its data analysis tool. However, it has data integrity issues and other drawbacks like a limited memory that make it less appealing to the enterprise clients. Power BI moves the capabilities of MS Excel to a whole new pedestal. It delivers an enticing experience of working with the tools like Power Query for data extraction and transformation. Power Pivot allows for data modeling and analysis, while Power View distinctly maps the data for visualization in unprecedented ways.

Firmly putting the power in right hands 

Power BI has contributed effectively to the analytics industry and helped improve some of its facets. With the offering from Office 365 subscribers, many organizations prefer to use Power BI due to its flexibility. Microsoft already has a pretty good presence in the analytics environment with its popular products like SSAS – SQL Server Analysis Service. With Power BI, the company only has only put the power in right hands. Microsoft hopes that other data analytics services will pick up Power BI Desktop to provide users some degree of interoperability between different services.

Why businesses are for Power BI

Power BI is more about the art of what is possible. You just take your data, deploy machine learning on it, build it into an app and extract the insightful business intelligence. It’s only a matter of time before Power BI became the tool of choice for business intelligence solutions for most of the forward-thinking, aggressively growing enterprises regardless of their size. It is firmly on its way to global dominance when it comes to business intelligence and analytics.

BI is used for multiple business purposes like:

• Quantitative analysis through predictive modeling, business process modeling, predictive and statistical analysis.
• Measurement of performance and benchmarking progress toward business goal.
• Developing collaborative programs through electronic data interchange (EDI) and data sharing.
• Developing knowledge management programs to create insight and experiences for regulatory compliance and learning management.
• Reporting of departmental perspectives of data visualization, OLAP and EIS.
• Getting visual answers to tricky business questions.

Business intelligence tools comprise of systems designed to capture, categorize and analyze corporate business data to gain insight for improved decision-making. The more advanced the system is, the more data sources it will combine to gather intelligence. These data sources include social media channels, internal metrics coming from different company departments, external data collected from third-party systems, and macroeconomic data.

A modern BI needs to fulfill the below criteria:

• It should provide total abstraction of end-to-end process so that the analysts are not bogged down by the system complexity
• It should have robust business modeling capabilities
• It should be able to deliver extreme performance from the distributed and commodity clustered architecture
• The system should be able to perform statistical and quantitative analysis using vast amounts of data
• The BI has to support limitless types of data sources

BI tools include data visualization software for designing charts, infographics and performance scorecards that display key performance indicators (KPIs) and visualized data in an easy-to-grasp way. Data visualization tools have become the standard of modern BI. Now, virtually every major BI tool incorporates features of visual data discovery.

BI programs include many forms of advanced analytics such as:

• Data mining
• Text mining
• Predictive analytics
• Statistical analysis
• Big data analytics

BI platforms are increasingly being used as front-end interfaces for big data systems. Users can connect to a range of data sources, including NoSQL databases, Hadoop, cloud platform and the more conventional data warehouses to obtain a unified view of their diverse data.

The potential benefits of BI include:

• Accelerated decision-making
• Enhanced operational efficiency
• Optimized internal business processes
• Identifying market trends and spot business problems
• Gaining competitive advantage over business rivals
• Enhanced productivity and collaboration

BI combines a broad set of data analysis applications including online analytical processing (OLAP), mobile BI, cloud and software-as-a-service BI, real-time BI, open source BI, location intelligence and collaborative BI. Business intelligence teams generally include a mix of BI developers, BI architects, data management professionals and business analysts. Advanced analytics projects are often managed by separate teams of data scientists, predictive modelers, data scientists, statisticians and analytics professionals while BI teams oversee more straightforward querying and analysis of business data.

Power BI – experience your data, anywhere, anytime

Microsoft Power BI is a suite of business analytics tools that delivers insights throughout an enterprise and allows the business managers to drive ad hoc analysis. It’s a free, self-service cloud service that provides non-technical business users with the tools for visualizing, analyzing, aggregating and sharing data. It’s user interface is fairly intuitive for those familiar with Excel, and its deep integration with other Microsoft products makes it a highly versatile tool. Every user can create their personalized dashboard with a 360 degree view of their business.

The service is available as a web-based software-as-a-service (SaaS), called Power BI. Its downloadable version for Windows is called Power BI Desktop. Native mobile apps for Android, iOS and Windows are also available.

Key features of Power BI are described below in brief:

Hybrid deployment support: Built-in connectors allow the tool to connect with multiple data sources from Microsoft, Salesforce, Facebook and other vendors.

Quick insights: The users can create subsets of data and automatically apply analytics to that information.

APIs for integration: The developers can use sample code and APIs for embedding the dashboard in other software products.

Cortana integration: This feature allows mobile users to verbally query data using natural language and access results using Cortana – Microsoft’s digital assistant.

Customization: The developers can change the appearance of default visualization and reporting tools, and import new tools into the platform.

With Power BI, the users can post Excel-based reports along with queries used to gather the data of the reports. Other users can access this information and use it to generate their own reports. Microsoft has made four plug-ins available to Excel that let the users retrieve heterogeneous data, create reports and integrate them with Power BI. These plug-ins are:

Power Query: Builds queries that connect to various data sources and supports sophisticated analytics with Excel.

Power Pivot: Creates complex table-based data models that support hierarchies, relationships and custom measures.

Power Map: Provides geospatial data on 3-D maps integrated into the reports.

Power View: Creates analytical reports including graphs and interactive charts.

Visualization features that go beyond Excel

Excel has many useful features for business intelligence but when you work with large volumes of data, you need more functionalities that Excel doesn’t have. Power BI for Office 365 combines the features of Power Query and Power Pivot currently available for Excel. With Power Query, non-programmers can do some of their own Extraction, Transformation and Load (ETL) operations. This feature comes handy for the analysts who want to integrate the organization’s business data with the publicly available data. Power Pivot allows the analysts to work with millions of rows of data at one time. Its visualization capabilities let you create the tools that others can use, like pivot tables.

Customized BI solutions for businesses

At Flexsin, we have experts who will integrate BI into your business intelligence strategy, allowing you to immediately start observing trends. Track the health of your business and make data driven decisions faster with our innovative business intelligence solutions. We partner with your business to deploy BI, and serve as your dedicated BI liaison. Our Power BI solutions will allow you to connect, model, clean and synthesize data, and create interactive live reports and dashboard.

The analytics landscape is evolving at an unprecedented speed. Apart from mobility and predictive analytics, one big innovation that has revolutionized analytics and opened a fresh world of data-driven possibilities is business intelligence (BI). This revolution in analytics has enabled enterprises to take an immersive approach to data. Here is how BI is making it possible for businesses to harness huge quantities of data intuitively.

Intelligence That Gives Data The Business Edge Of Tomorrow

Driven by the raw power of technology, BI gives the most actionable information and insights that let next-gen corporate end-users, managers, and executives make informed business decisions. BI uses the right set of tools, technologies, and methodologies for creating fresh intelligence and for unlocking the hidden or trapped data value available at the core of enterprises.

A powerful BI solution is the one empowering businesses to collect data from a variety of external and internal sources. Once the data is accumulated, a BI solution will prepare the analysis; develop queries and run them on the data; and finally create easy-to-use reports, infographics, and dashboards for making the right analytical results available.

Businesses, today, are investing in prebuilt BI solutions that are engineered to deliver role-based, intuitive intelligence for senior management and front-line employees. One such powerful tool that has made changed the way BI works is Power BI. True to its name, Power BI empowers business intelligence so that the distance between data and insights is reduced.

Power BI Enables Data To Drive Critical Decisions

Microsoft’s Power BI has a range of business analytics tools for delivering the best insights across an enterprise. This solution makes it possible for businesses to travel from raw data to actionable insights in just minutes. The suite of business analytics tools is able to connect nearly hundreds of disparate data sources that not only simplify data prep but also drive ad hoc analyses to the next level.

More forward-thinking companies are embracing Power BI for a number of factors. Some of them include accelerating decision-making, improving the quality of the decisions, increasing operational efficiency, optimizing internal business processes, and generating fresh revenue streams.

This sophistication of Power BI solutions even enables companies to identify the growing market trends and pick fresh business opportunities waiting to be explored. So with highly evolved Power BI, a business can expect to transform data into live reports and dashboards that answer an enterprise’s mission-critical questions.

Before Power BI Was Mainstreamed Into The Business World

Before Power BI existed, businesses that were on the fast track to evolution and growth faced challenges while working with huge chunks of valuable enterprise data. Because they mismanaged their respective datasets, businesses created road blocks in their transformational journey.

Also, the enterprises that did not harness the power of data for driving business decision-making relied on siloed data alone. Such data would have been impartial because it remained in isolation, and any impartial dataset lacks the capability to empower decision-makers. That is when the need for an intelligent solution was felt; that is when the need for Power BI manifested itself in full form.

The raw power of this BI solution to deliver the most relevant insights and analyses with low cost of ownership is noted by Gartner. For the tenth year in a row, Microsoft is positioned in the Leader section in Gartner Magic Quadrant for Business Intelligence and Analytics Platforms.

What Empowers The Power BI To Deliver Actionable Insights?

Power BI enables data analysis to be agile, quick, and user generated. This is one tool that not only simplifies but also takes data collaboration, analysis, and sharing to the next level. Here are the top three components that empower the performance of this BI solution at every step of analysis.

Power Pivot

This component imports and even integrates the datasets from a variety of sources for developing in-memory data models. Such integration enables any functional user to combine different data sources for improving its overall value. Classic examples of such integration can be found when demographics data or weather data or corporate sales data is analyzed using Power BI. This component, further, supports complex calculations and aggregates hierarchies, key performance, and indicators. And it can even be used by analysts for data prototyping and for doing a one-time analysis of different business situations.

Power Query

Also known as Data Explorer, this component searches for data within different corporate data sources or online. The dataset picked by Power Query is seamlessly imported on an Excel table, and this is doubtlessly a game-changing feature for many analysts. This component offers native data connectivity that is distinctive and that makes data access simpler and quicker than ever. Tasks such as renaming columns, merging data, replacing values, and doing other critical data modifications are done by this component. This component becomes a big deal because it lets data shaping and cleansing without any hassles.

Power View

This is the go-to component when it comes to visualizing the data and making it more interactive than before. This tool is supposed to do things such as cross-filtering and highlighting the data. Working with the data in Power View bears a striking resemblance with working with PowerPoint and Excel Pivot Tables. Apart from the graphs, tabular data, and charts, Power View supports different maps having the zoom and pan capabilities as they are integrated with Bing Maps.

Power BI Is Revolutionizing The Business Intelligence Landscape

If a business lacks the capabilities of delivering key insights, it will lack a proven competitive edge and will experience sluggish sales. And when it is about laying the hands on the most actionable business insights, it is certainly about Power BI. This business insights tool from Microsoft changes big data into immersive visualizations and interactive reports that can be consumed at a glance. Now, here are the top ways through which Power BI has rightfully revolutionized the world of analytics and business intelligence.

Data Becomes Accessible And Organized

Previously, Power BI is engineered to integrate a variety of Microsoft Tech such as SQL databases and SharePoint. Now, the solution is even taking data from non-Microsoft resources. Right now, Power BI has the ability to connect and integrate big data, streaming data, on-premise data sources, Excel spreadsheets, and cloud data. So no matter where the enterprise data lives, Power BI has the ability to merge and analyze it. To date, the BI solution is known for connecting hundreds of data sources seamlessly. This BI solution can easily extract, connect, and analyze data from the following sources.

• MySQL
• Excel
• Google Analytics
• Azure Analysis Services
• CSV
• Oracle
• Salesforce
• MailChimp
• Web Pages
• Microsoft Dynamics CRM
• Azure SQL Database

Taking The Stress Out Of Implementation

Very few IT resources and little engineering abilities are required for completely implementing Power BI across an organization. In fact, there are a few instances that will not require any engineering ability for the end-users. Managers simply need to develop an API key and plug that inside the software. If the enterprise is using Microsoft systems such as Office 365, then using Power BI will be natural and simple. That is because this BI solution easily integrates with Microsoft Teams and Office 365 groups.

Experience Robust Security Every Time

Power BI uses Active Directory for setting up the access to the control panel; only through this panel, the organization will use different Microsoft solutions. Apart from creating a traditional security layer, Power BI is designed for developing row-level security that enables the team to rescind and grant access in a typically controlled environment. This will not only make the data more secure but also improve a report’s usefulness.

Simplifying The Learning Experience

The biggest advantage of Power BI is that its learning curve is not at all steep. Nearly everyone leverages Microsoft products, so the user interface along with the ribbons will be easy to use for a number of users. Because of this, users can easily unleash the capabilities of Power BI; and expect power users to jump straight into discovering advanced data modeling techniques that are made possible by this solution.

Applying The Insights Of Power BI To Create Digital Capabilities

Management

Power BI has a stream of cloud-based and desktop tools that bring corporate data to life. These tools let an enterprise’s management to analyze and visualize data with improved understanding, efficiency, and speed. This suite of applications easily connects the top brass with a wide range of data through interactive reports, compelling visualizations, and simple-to-use dashboards.

Analysts

With this solution, the analysts can move from data to insights to actions in no time. The analysts use Power BI to connect and integrate information from a variety of data sources. All of this ultimately helps the analysts analyze complex data streams and create insights and reports in minutes.

Business Users

Power BI allows sales workforce to know where the next big opportunity is hiding in the ever-evolving business landscape. The business users will have the access to powerful dashboards not only on the web but also through the app. So with this tool, the staff will have the right data and insights at their fingertips always.

IT Personnel

The raw power of this revolutionary solution may even be used by a business’s IT personnel. With Power BI, IT team will be able to simplify data management, get the required compliance, and make data absolutely secure by giving the employees only the right insights at the right moment.

Development Teams

Power BI is engineered to support the data-driven culture that is becoming more common with apps these days. This BI solution can be easily embedded on Azure for letting APIs develop and deliver the right analytics for making the best possible decision. This is a one-of-its-kind BI solution that energizes any enterprise-grade application by letting it include immersive visuals and fully interactive reports. So if developers need any data to make a powerful app, they actually need Power BI by their side.

So for building a data-intensive enterprise, managers and decision-makers need to manage complex information flows. And the best way to make data the biggest corporate asset is Power BI. This is the only advanced solution that can build the analytics workforce of tomorrow.

Hadoop-based solutions revolutionize the way big data is conceived, stored, processed, and transformed into a competitive edge. The power of Hadoop is evident as influential Web 2.0 companies, such as Facebook, Google, and Yahoo!, deploy it for handling terabytes of poly-structured data sets.

Being an open-source framework, Hadoop unleashes the power of distributed processing to streamline big data. Apache Hadoop has emerged as a de-facto infrastructure for managing huge data lakes because of its powerful scalability and matchless cost-effectiveness. And here is how this infrastructure is evolving steadily to change the vast big-data landscape for the better.

Scalability Going Beyond Excellence

Hadoop has improved its scalability by capping the data’s throughput and restricting the data’s flow to a single server. Because of this, the framework distributes and stores vast amounts of data sets across thousands of inexpensive servers operating simultaneously. Unlike many traditional RBDMS’s that fail to scale huge data sets, Hadoop, on the other hand, empowers enterprises to execute applications on a number of nodes that involve hundreds of terabytes of unstructured data.

Every scalable Hadoop solution will always keep the data traffic to a minimum, and this will even not let the network face massive file bottlenecks. The framework’s distributed-processing capabilities allow it to handle large data clusters among a number of hardware commodities.

However, if Hadoop development services face a few scalability oversights, then the entire implementation lifecycle may have to face expensive changes. In short, this open-source framework reduces the overall quantity of nodes while maintaining the most demanding data-storage requirements.

A Cost-Effective Solution For The Future

Hadoop is rising as a cost-efficient alternative to a number of traditional extract, transform, and load (ETL) processes; these costly processes or modules extract data from a number of systems, converts it into a structure for streamlining analyses and reporting, and loads it on databases.

Businesses new to big data will see this concept overwhelming the conventional ETL processes. This is where Hadoop, a true cost-effective data management tool, comes in. This open-source framework can process massive data volumes easily and quickly; that is something even the most efficient RDBMS’s cannot do because they are cost prohibitive.

Hadoop is engineered as a completely scale-out architecture that can store a company’s raw data, which can be used later. The cost savings coming with the deployment of this framework are staggering. In an RDBMS, it costs nearly tens of thousands of dollars to process every single terabyte; Hadoop, instead, offers magnificent storage and computing capabilities that can cost businesses just a few hundred dollars per terabyte.

Speed Up The Performance

Hadoop accelerates data processing, which is ideal for environments that face a high influx of raw data. Businesses that are looking for thriving in data-intensive, large-scale atmosphere should opt for Hadoop because of its speed. This framework deploys unique storage method involving a distributed file system that maps every single data set with its location inside a cluster.

In Hadoop, the data-processing tools are nearly always located within the same server that carries the data. Because of this proximity, data processing is generally superfast even for large deployments. Apart from this, the framework even uses Hadoop Distributed File System and MapReduce programming models relying on a fully scalable storage mechanism.

So if a business is dealing with the management of totally unstructured data sets, then it should leverage the power of Hadoop. That is because this is the only platform that can process data worth ten terabytes in a couple of minutes. Consider petabytes of data to be processed within hours if Hadoop is used.

Flexibility Improves Framework Credibility

With Hadoop, businesses can easily modify data systems as and when their needs and environments change. Hadoop’s flexibility allows it to link a number of commodity hardware including off-the-shelf systems. Because of its open source, everyone is free to change the way Hadoop does certain functions. This capability to modify the framework, in turn, has improved the framework’s flexibility.

Hadoop lets businesses access fresh data sources and discover different data sets (both structured and unstructured) that can be used for drawing fresh, valuable insights. So whether the data is coming from a company’s social-media channels or its email conversations, Hadoop can process it all with improved credibility and unmatched flexibility.

Also, the framework can be leveraged for a range of purposes such as warehousing data, recommending systems, processing logs, analyzing market campaigns, and detecting fraudulences within a system. Due to its multitasking abilities, this framework is admired for its flexibility among different corporate houses.

These benefits are even new to some of those organizations having matured processes. So if a business needs to experience true benefits of data processing, it will have to work with Hadoop. All those enterprises that are still new to this framework should leverage Hadoop consulting services. Such consulting and development services will let companies use this infrastructure to build a safe data-management environment.

Businesses that invest in big data have generated revenues, enhanced customer experience, improved enterprise-wide performance, and developed new markets. Data lakes are becoming large with each passing second, so managing these unorganized data sets is becoming even more complicated. And the fact is that the proper management of these data lakes is the only gateway to digital transformation.

Today, businesses know that data is nowhere near stopping, so it is better if they transform the way they manage data. For managing data efficiently and for drawing key insights from gigantic data lakes, it is essential to use the right technological tools and the right big data solutions and strategies.

Technological Frameworks That Enrich Big-Data Experience

The big-data architecture is evolving continuously. So to keep up with this fast-paced evolution, businesses should invest in technologies such as MongoDB, Apache Hadoop, and NoSQL. Here’s a short introduction on each one of them, now. Businesses should go through these guides to analyze which framework will suit their needs.

Apache Hadoop

When it comes to processing large-scale data, Apache Hadoop comes into play. This software framework, which is mastered by a number of Hadoop developers, is an open-source platform that has a number of individual modules including a resource management platform, a large-scale programming model for processing components and data that give high-level interfaces, and a distributed file system. Last, this framework is written in Java.

MongoDB

On the other hand, MongoDB is purely written using C++ and it belongs to the family of NoSQL. Another big difference that any business will feel when it uses MongoDB is its unconventional model. This model completely avoids companies to create a table-based structure that is generally found in relational databases. Doing ad hoc queries is this framework’s forte, and this enables the DBMS to execute granular-level searches. This database also features load balancing, indexing, server-side script execution, replication, and aggregation.

Powerful Platforms Unlock Big-Data Value

Managing big data using just a single framework is nearly impossible. Because of big data’s complexity, it is important to use an array of these technologies together—that is why many companies rely on MongoDB database managers and Hadoop consulting services before mixing the two technologies.

Companies, today, rely on both Hadoop and MongoDB; these two technologies can easily replace the conventional RDBMS’s and can even perform better than them. Both of these technologies are engineered to manage vast data sets with matchless efficiency. By using the Hadoop framework and the MongoDB database, companies will find it easier to execute large-scale real-time processing.

Optimize The World Of Big Data, Together

While developing real-time big-data applications, MongoDB powers online and serves end-users and business processes all the analytics models that were created using Hadoop. These analytical models, once analyzed, can give users keen insights into complex operational processes.

Hadoop and MongoDB take big-data initiatives to mix multiple data streams coming from different origins. This blend is leveraged to create sophisticated analytics and powerful machine learning models. Afterward, MongoDB receives the results that can be used for designing and deploying transformative data models.

Make the most of the new opportunities that evolve with big data by picking a reliable technology partner. The right data-science talent will have the expertise in offering Hadoop development services and MongoDB consulting solutions for the ever-changing big-data landscape.

Nearly two decades ago, data scientists and analysts forecasted that data sets would grow rapidly. This forecast is proven right and has even revolutionized data management lifecycles. And, today, any data scientist can assure businesses that this data flow will not cease anytime soon.

This assurance implies that big data will get stronger with each passing day—and that has raised many challenges in managing complicated data sets. For many data analysts, tools such as NoSQL, MongoDB, and Apache Hadoop are used for representing big data. But as today’s data architecture is getting bigger than ever, it is important to pick a tool that rightfully maps such complex data sets.

Two tools that can streamline and even reimagine the way businesses manage data today include MongoDB and Hadoop. Because of this, the post covers every single factor why companies—struggling with data swamps—should focus their time and resources on these two platforms.

Apache Hadoop

As an open-source framework, Hadoop stores and processes large-sized data sets with effortless ease. This framework has individual modules that can carry a wide, Hadoop Distributed File System (HDFS). Further, the Java-based framework is engineered to include a sophisticated programming model for processing data quickly, a set of components providing high-level interfaces, and a resource management platform.

Hadoop Components

• Hadoop Common: A collection of utilities and libraries dependent on countless other Hadoop modules.

• HDFS: A fault-tolerant, scalable file system that is written in Java and that is designed to offer high throughput to large-sized application data sets.

• MapReduce: This is a software development framework/paradigm for easily processing huge chunks of data sets simultaneously.

• Yet Another Resource Negotiator: This is a robust framework for handling or scheduling resource requests coming from distributed applications.

Benefits of leveraging Hadoop

• Scalable: This framework is known for its stability as it can efficiently store and distribute large-sized data sets across a range of inexpensive servers operating in parallel.
• Cost effective: Then compared with the traditional RDBMS, Hadoop takes few amounts of commercial resources and energy to process large volumes of data.
• Flexible: Any leading big data solutions provider will vouch for Hadoop’s flexibility. This platform is designed to tap into a variety of data sets (both unstructured and structured) and to generate value from them.

MongoDB

This platform is written in C++ and belongs to the core family of NoSQL. The framework is based on an unconventional model, and that is why it avoids relational database’s table-based structure. Whether it is about indexing, aggregation, or replication, MongoDB can execute all such tasks in short turnarounds.

Core components

• Mongod: This component acts as the core database process of this framework.
• Mongos: Here is a query router and controller for managing shared clusters easily.
• Mongo: This component is an interactive MongoDB Shell.

Benefits of using MongoDB

• Schema less: This platform is a document database where one collection can simply hold a number of documents.
• Document-oriented storage: This benefit is one of the favorites of enterprise big data services providers where data is stored efficiently within JSON–styled documents files.
• Content management: If a business is concerned about managing complex content flows and handling humongous data sets, then this platform should be deployed without ado.

Because of these factors, MongoDB and Hadoop have become reliable platforms for managing big data for any company. If a business is grappling with getting crucial insights into mismanaged data lakes, then it should get in touch with a credible big data solutions provider today.

Data is among the biggest assets that a company has. Once the business analyzes its gargantuan data sets—both structured and unstructured—it can gather insights into:

• Customers’ buying patterns
• Root causes of issues, defects, and failures within the shortest turnaround
• All the fraudulent behaviors before they start affecting a company
• The authenticity of a risk portfolio within minutes

In short, big data has become a very important space (which is quickly evolving) as corporate players leverage it to design their strategies. That is, every business must be interested in knowing the big data trends that dominate now. For that reason, here are some top big data fads that are nowhere near to fading themselves this year.

Hadoop

Being an open-source framework to store and to process big data chunks, Hadoop has a lot gained prominence today. A recent survey covering close to 2,200 Hadoop customers had the following findings to share:

• Only 3 percent of the respondents reported that they think of doing less with their respective projects.
• Close to 76 percent of the respondents who are slated to begin a Hadoop project will continue to do more (with this framework) in the next 3 months.
• Half of the companies that have not worked with Hadoop are likely to deploy this platform this year.

Cloud-based data warehouse

The “death” of data warehouse has been talked a lot, but it is a bit overhyped as the technology is nearly flourishing on the Cloud. Data warehouse, the technology, has indeed stepping toward obsoletism with a rapid space.

However, the technology is getting resurrected on the Cloud as Amazon, too, introduced its Cloud-based on-demand data warehouse (which is referred to as Amazon Redshift). Nevertheless, this petabyte-scale Cloud-based data warehouse is fast to find a robust competition in Google’s BigQuery and the long-time champion Azure SQL Data Warehouse from Microsoft.

According to leading analysts, nearly 90 percent of firms that have adopted Hadoop will rely on these virtual warehouses. (That is because with these Cloud-based data-warehousing technologies, the corporates can dynamically and easily scale up or down the computer resources or the amount of storage space required.)

NoSQL

2015 saw a rapid increase in the adoption of various NoSQL technologies as they are generally associated with unstructured data sets. This year, however, companies (that have relied on NoSQL in the past) have gone a step further as they are ready to embrace the NoSQL databases.

The NoSQL databases have become one of the cornerstones of the Enterprise IT landscape because the merits of deploying a schema-less database become more and more prominent. This particular fact has gained ground as the latest Gartner’s Magic Quadrant states that the recent NoSQL firms such as DataStax, Redis Labs, MongoDB, and Amazon Web Services have easily outnumbered the run-of-the-mill database vendors (Oracle, IBM, SAP, and Microsoft).

As big data is still evolving and as new trends related to this space are still surface, it is sometimes difficult to leverage this technology effectively. For this reason, it is better when a business can leverage big data consulting services before making sense of its countless structured and unstructured data sets.