Big Data Analytics- Get into The Crux of The Matter

Big Data Analytics- Get into the crux of the matter

Do you think that big data is just a buzzword? Well, it has become a new fact of business life. All you need to do is strategize appropriately for managing large volumes of both structured as well as unstructured data. The good news is that you will come across a plethora of tools and technologies that will help you to identify trends, detect patterns and glean other valuable findings from the sea of information available to them. But are they sufficient enough?

Being new to big data, it can be quite tempting to go and buy a big data analytics software thinking it will act as an appropriate answer to your company’s business needs. But honestly speaking, choosing a technology or a tool, in particular, is just not enough, you also need to hire a prominent big data analytics company in Melbourne that follows well-planned processes and incorporates a bunch of skilled and talented people who are capable enough to leverage the most out of such technologies.

Big data analytics — Technologies and Tools

First of all, let us understand the term big data analytics? It is the process of extracting useful information by analyzing different types of big data sets. In a layman’s language, big data technology is used to discover various hidden patterns, market trends, and consumer preferences, for the benefit of organizational decision making.      

  • Apache Hadoop

The Java-based free software framework has the potential to store large amounts of data in a cluster. These kinds of frameworks run in parallel on a cluster and have ability to allow us to process data across all nodes. Do you know what Hadoop Distributed File System (HDFS) is? It is a storage system of Hadoop which splits big data and distribute across many nodes in a cluster. Also, it replicates data in a cluster providing high availability.

  • Microsoft HDInsight

Powered by Apache Hadoop, this big data solution is available as a service in the cloud. HDInsight uses Windows Azure Blob storage as the default file system. And this again provides high availability with low cost.

  • NoSQL

Traditional SQL is most of the time used to handle a large amount of structured data but for unstructured data, we need NoSQL (Not Only SQL). NoSQL databases store unstructured data with no particular schema. Each row can have its own set of column values and I personally believe that NoSQL gives better performance when it comes to providing a massive amount of data.

  • Hive

Hive is a distributed data management for Hadoop. This supports SQL-like query option HiveSQL (HSQL) to access big data. This can be primarily used for Data mining purpose. This runs on top of Hadoop.

  • Sqoop

The tool, in particular, has the potential to connect Hadoop with various relational databases when it comes to transferring data. Apart from this, it can be effectively used to transfer structured data to Hadoop or Hive.

  • PolyBase

Works on top of SQL Server 2012 Parallel Data Warehouse (PDW), it is used to access data stored in PDW. The tool can be considered as a datawarhousing appliance built used to process any volume of relational data and provides an integration with Hadoop allowing us to access non-relational data as well.

Challenges of Big Data Analytics

Early big data systems were mostly deployed on-premises, particularly in large organizations that were collecting, organizing and analyzing massive amounts of data. But cloud platform vendors, such as Amazon Web Services (AWS) and Microsoft, have made it easier to set up and manage Hadoop clusters in the cloud.  

The concept of big data is widening across the globe and developed countries like Australia has started making the most out of the technology. Now every coin has two sides and big data is no exception. It includes analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy like challenges which needs to be taken into consideration. In addition, integrating Hadoop, Spark and other big data tools into a cohesive architecture that meets an organization’s big data analytics needs is a challenging proposition for many IT and analytics teams, which have to identify the right mix of technologies and then put the pieces together.

So that’s all for now! I hope the post provides you some help!

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