Open-source Tools For Big Data Analytics Solutions For Smes

By Author: Steve
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The growing buzz around big data may have contributed to the concept that it is only applicable for large enterprises. However, this is not the case as even SMEs stand to gain insights with the right combination of tools and technologies. For SMEs to leverage big data and analytics, there are a numerous open source solutions as well that would hardly cause a dent in their existing budgets.

Analytics Database:

Selecting an appropriate database model is essential to address the processing requirements of any business organization in real time. Apart from the basic batch processing offered by a Hadoop Distributed File System (HDFS), there are three popular NoSQL database models more effective in handling the unstructured data as compared to the traditional Relational databases.

1. In-Memory or Columnar Database:

The data stored in in-memory or columnar databases are highly compressed thereby ensuring efficient read and write operations. Areas like content management, event loggings, visitor countering or blogging platform services find application with this form of database. Cassandra, Hypertable, Google ...
... Big Table and HBase are some popular open-source databases of this type.

2. Graph Database:

Similar to a graph structure, a graph database uses edges, nodes and properties to store as well as represent data. The graph databases are more suitable for routing, linked data, location-based or recommendation services. A few popularly utilized graph databases include Neo4J, HyperGraphDB, Orient DB and Infinity Graph.

3. Document Database:

A document database stores documents in the form of key-value pairs. The document database is effective for applications like electronic commerce, real time analysis or simply web analysis. A few popular examples of a graph database include systems like MongoDB, Terrastore, Couch DB and Orient DB.

Apart from these forms of databases, the SMEs can in effect bypass the Hadoop Distribution File System (HDFS) to implement a standalone NoSQL database as well. The basic HDFS is suitable for batch data processing.

Processing Data:

Big data analytics solutions are all about processing large incoming sets of raw information in multiple formats and structures. Some popular open source tools addressing multiple information sets are:

1. Apache Storm: Handles data triggered by events and is a general-purpose event-processing system.

2. Hive: Queries data using an SQL-like language for ad-hoc querying or analytics purposes on HDFS structures.

3. Flume: Distributed system to aggregate, collect or move large datasets from multiple sources into other central data storage or HDFS.

4. Sqoop: Transfers datasets from a relational database to Hadoop Distribution System.

Analyzing Data:

In a survey report by Capgemini, 79% organizations have been unable to completely integrate their data sources across the enterprise. The relevance of big data is lost if enterprises fail to unlock the insights offered by big data in real time. With the prevalence of open source tools like Google Big Query, Apache Mahout, Pentaho, R or Apache Spark, SMEs (or even large-scale enterprises) can effectively implement them for real time and ad hoc querying activities.

The effectiveness of an effective analytics solution is not lost on even the mobile app company. These app development companies, often in the category of SMEs, have full-scale analytics platforms in place to assist their clients in formulating well-informed decisions.