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Top 10 Trends In Big Data

An increasing number of organizations are collecting, storing, processing and analyzing data in various forms. The number of systems that will support larger volumes of structured and unstructured data will rise gradually.

The market is poised to help data handlers operate and secure Big Data and will enable consumers to analyze the data. Let us highlight the top trends and chart the evolution of Big Data.

  • Big Data to become fast and approachable

Interactivity is the need of the hour and several Big Data databases are rapidly embracing interactivity to streamline functioning. Businesses are increasingly adopting faster databases like MemSQL, Hadoop-based stores such as Kudu and faster query accelerators.

  • Purpose-built tools for Hadoop become obsolete

In earlier years, several technologies were introduced to fulfill the need for data analytics on the Hadoop framework. However, enterprises no longer want to adopt an outdated Business Insights access point for a single Hadoop data source. Enterprises are alternatively looking to gain access to cloud warehouses, as well as structured and unstructured data from both Hadoop and non-Hadoop sources.

Customers will ultimately want high level analytics on all data and to stay relevant. Analytics platforms need to be built to function across different use cases, rather than only on Hadoop.

  • Data Lakes will drive value for businesses

You must be wondering: What is a Data Lake? Let’s help you understand a Data Lake.

A Data Lake is similar to a man-made reservoir. Like how you build a dam across a water body, you create a Data Lake by building a cluster. Then, you let it fill up with water or data. As soon as the lake is created, you begin to use the water or data stored in it fir different purposes such as predictive analytics, Machine Learning and cyber security among others.

  • The need for flexible architecture in analytics tools

Business organizations are evolving to realize that data analysis platforms such as Hadoop, for instance, need to be flexible to work on several types of use cases. When they create data strategy, businesses are researching and evaluating various factors such as user personas, questions, volumes, access frequency and speed of data before they build a robust strategy for their organization. Businesses now require flexible platforms which can be easily reconfigured to match evolving needs and use cases.

  • Variety drives investments in Big Data

According to Gartner, Big Data can be defined as the three Vs: high-volume, high velocity, high-variety information assets. While all three Vs experience growth, variety is fast becoming the single biggest driver of investments in big-data.

Organizations will continue to evaluate analytics platforms based on their ability to provide live direct connectivity to disparate data sources.

  • Spark and Machine Learning helping the growth of Big Data

Apache Spark is quickly becoming the chosen Big Data platform among data architects, BI analysts, and IT managers. Platforms such as MapReduce that do not offer support for interactive applications or real-time stream processing are being left out in the cold. As Machine Learning continues to gain popularity, the focus of data analytics will shift to self-service software providers in an attempt to make data more accessible to the end user.

  • The Internet of Things (IoT), cloud and Big Data converge to create new opportunities for automated analytics

The Internet of Things is generating huge volumes of structured and unstructured data, and an increasing amount of this data is being shared on cloud services. There is a growing demand for next generation analytics tools that can seamlessly connect to and combine a variety of data sources hosted on the cloud. These flexible, futuristic tools allow businesses to explore and visualize data in unique ways, ultimately enabling them to discover unknown opportunities in their IoT investment.

  • Preparing data for analysis must become easier

Businesses largely want to reduce the time and complexity involved in preparing data for analysis. Easy-to-analyze data is very important especially when you are dealing with several types of data and different formats.

Self-service data preparation tools allow the data to be made available as snapshots. This facilitates faster and easier exploration of the data.

  • Companies expect enterprise-level capabilities from data analysis platforms

Organizations are increasingly looking for ways to apply consistent data classification across the data ecosystem. Businesses are now looking for analysis platforms that offer high-end capabilities that function across data types.

  • Automated data discovery for analysis

Earlier, business organizations would be inundated by the extra data they used to get access to. Now, they can process large volumes of data. However, the problem is that the data is generally not well organized and difficult to locate.

The Last Word

Several companies like Alation and Waterline are using the capabilities of Machine Learning to automate the task of finding data suitable for analysis. They catalogue the files with tags, discover relationships between data assets and offer query suggestions via searchable user interfaces. In the future, we will see a growing demand for self-service data discovery which will be an extension of self-service data analytics.

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