Big Data Storage


Big data storage is being used by companies of all sizes to not only hold massive amounts of data, but to also run analytics on those data sets.

This large-scale statistical analysis of data or its metadata can bring organizations an advantage over their competitors when applied effectively. In big data environments, analytics operate mostly on a circumscribed set of data, using a series of data mining-based predictive modeling forecasts to determine customer behaviors or the chance of future events.

This form of statistical big data analysis and modeling is gaining adoption across a number of industries, including financial markets, aerospace, energy exploration, environmental science, genomics, retailing and healthcare. Big data platforms are designed for much greater scale, speed and performance than traditional enterprise storage. Additionally, in most cases, big data storage targets a much more limited set of workloads on which it operates.

big data analytics

Ideally a big data storage system will have the ability to store a virtually unlimited amount of data, be flexible enough to deal with a broad spectrum of different data models, support both structured and unstructured data, handle high rates of both random read and write access, and for security reasons, only work on encrypted data.

This scenario, of course, cannot be fully realized. However, over the years a number of big data storage technologies have emerged that at least somewhat address these challenges. Each of these storage technologies in some way address the volume, velocity, or variety challenge of big data storage.

apache hadoop

The Apache Hadoop Distributed File System (HDFS) is perhaps the most popular analytics engine for big data, and is typically combined with some flavor of a NoSQL database.

Apache Hadoop is a java based free software framework that can effectively store a large amount of data in a cluster. This framework runs in parallel on a cluster and has the ability to process data across all nodes. Hadoop Distributed File System (HDFS) is the storage system of Hadoop which splits big data and distribute across many nodes in a cluster. This also replicates data in a cluster thus providing high availability.


Big Data Storage and Analysis Tools


A few other common big data storage and analysis tools include the following:


NoSQL, or Not Only SQL, is an approach to database design that is designed to accommodate a wide variety of data models, including value, columnar, document and graph formats. Traditional SQL can be effectively used to handle large structured data sets, however in order to handle unstructured data, you need NoSQL. NoSQL databases store unstructured data with no specific schema.

Apache Hive

Apache Hive is data warehouse software that facilitates reading, writing and managing large datasets residing in distributed storage using SQL. It can be primarily used for data mining and runs on top of Hadoop.


Sqoop connects to Hadoop to efficiently transfer bulk data to structured data stores such as relational databases. It supports incremental loads of a single table or a free from SQL query as well as saved jobs. The saved jobs can be run numerous times to import updates made to a database since last import. Additionally, imports can be used to populate tables in Hive or HBase, while exports can be used to pace data from Hadoop into a relational database.

Azure HDInsight

HDInsight is a big data tool from Microsoft which is a cloud distribution of the Hadoop components from the Hortonworks Data Platform (HDP). HDInsight uses Azure Blog storage as its default system and provides high availability at a low cost.


Presto is a project that was started by Facebook in 2012 and was released for Apache Hadoop in 2013. It is an open source distributed SQL query engine that allows you to run interactive analytic queries against a range of data sources, from gigabytes to petabytes. Unlike Hive, Presto does not depend on MapReduce technique and can quickly retrieve data.


PolyBase integrates Microsoft’s MPP product and SQL Server Parallel Data Warehouse (PDW) with Hadoop. In SQL Server 2016, it enables you to run queries on external data in Hadoop or import and export data from Azure Blob Storage. Queries are optimized to push computation to Hadoop. In Azure SQL Data Warehouse, you can import and export data from Azure Blob Storage and Azure Data Lake Store.

At Intersys, our large group of highly skilled consultants are exceptionally qualified in many different types of big data storage tools and use cases. With our expertise, we can implement or support all of your big data storage requirements and help your organization on its journey towards digital transformation.