Big data analytics technologies and tools

Unstructured and semi-structured data types typically don’t fit well in traditional data warehouses that are based on relational databases oriented to structured data sets. Further, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually, as in the case of real-time data on stock trading, the online activities of website visitors or the performance of mobile applications.

As a result, many of the organizations that collect, process and analyze big data turn to NoSQL databases, as well as Hadoop and its companion data analytics tools, including:

• YARN: a cluster management technology and one of the key features in second-generation Hadoop.
• MapReduce: a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors or stand-alone computers.
• Spark: an open source, parallel processing framework that enables users to run large-scale data analytics applications across clustered systems.
• HBase: a column-oriented key/value data store built to run on top of the Hadoop Distributed File System (HDFS).
• Hive: an open source data warehouse system for querying and analyzing large data sets stored in Hadoop files.
• Kafka: a distributed publish/subscribe messaging system designed to replace traditional message brokers.
• Pig: an open source technology that offers a high-level mechanism for the parallel programming of MapReduce jobs executed on Hadoop clusters.

How big data analytics works

In some cases, Hadoop clusters and NoSQL systems are used primarily as landing pads and staging areas for data before it gets loaded into a data warehouse or analytical database for analysis — usually in a summarized form that is more conducive to relational structures.

More frequently, however, big data analytics users are adopting the concept of a Hadoop data lake that serves as the primary repository for incoming streams of raw data. In such architectures, data can be analyzed directly in a Hadoop cluster or run through a processing engine like Spark. As in data warehousing, sound data management is a crucial first step in the big data analytics process. Data being stored in the HDFS must be organized, configured and partitioned properly to get good performance out of both extract, transform and load (ETL) integration jobs and analytical queries.

Once the data is ready, it can be analyzed with the software commonly used for advanced analytics processes. That includes tools for:

• Data mining, which sift through data sets in search of patterns and relationships;
• Predictive analytics, which build models to forecast customer behavior and other future developments;
• Machine learning, which taps algorithms to analyze large data sets; and
• Deep learning, a more advanced offshoot of machine learning.

Text mining and statistical analysis software can also play a role in the big data analytics process, as can mainstream business intelligence software and data visualization tools. For both ETL and analytics applications, queries can be written in MapReduce, with programming languages such as R, Python, Scala, and SQL, the standard languages for relational databases that are supported via SQL-on-Hadoop technologies.

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