Leveraging the Big Data Analytics Tools
One of the crucial challenges witnessed by organizations is to garner information from the big data’s that are contained in the data silos and is present all over the business units and company functions, such as marketing, contact center and sales. The data vaults avert the decision makers from attaining unified views of consumer and functional data. In addition to that, it confuses the initiatives of the organizations to make the most of full range of available information that can offer valuable insights into emerging consumer trends or market shifts that can quickly acted on for optimizing business performance. The data silos result in innumerable barriers that impede the decision making and entrepreneurial performance. One can easily loose the track of the real driver behind big data analytics and schedules business analytics that is obtained from an ongoing state of business situations that decision makers are attempting to understand and respond. Today the big data analytics tools offered by the software development service providers differ from one another. For instance, enterprise Big Data systems concentrate on data consistency and data cleansing, whereas the SEB’s and ISV’s are way more tolerant of inconsistencies. The enterprise Big Data systems puts greater importance on the licensed software and hardware based solutions than open source and public cloud. Enterprise database companies are fast making aggressive strides to expand their reach to monitor tetrabytes of data approaching the Big data scale. The focus areas are as follows:ISV & SEB Big Data Systems Distributed databases: MongoDB, Couchbase / CouchDB, Casandra Distributed file systems: HDFS, S3 Data warehouse: Hive/Pig, Amazon Redshift Distributed data processing: Hadoop Stream processing: Storm, node.js Search & indexing: Solr / Lucene, Elasticsearch, custom data crawlers Dual-Use Big Data Systems (ISV/SEB & Enterprise) Reporting: BIRT, Jaspersoft Data mining, analytics, and modeling: R, SAS, Microstrategy, Pentaho, SciPy, BI Velocity ETL and data management tools: Informatica, Kettle, IBM DataStage, Power Center, MS SSIS, Oracle PL/SQL, TalendOS, Sqoop Log acquisition, processing, and analysis: Flume, Splunk, graphite, logstash Work scheduler: Oozie, ActiveBatch Enterprise Data & Analytics Systems Relational databases: Oracle, MySQL, IBM DB2, Sybase, MS SQL Server MDM: IBM Initiate, Hyperian, Talend Analytical databases: Vertica, Infobright, MarkLogic Analytics: MS SSAS, Cognos Reports and dashboards: Cognos, MS SSRS, Crystal Report, QlikView, OBIEE, MicroStrategy, Tableau, High Charts, iFreeCharts Big data analytics tools helps companies to generate excellent business and operational gains. In addition to that, the data silos continue to restrain the potential that can be attained in a 360-degree view of consumers and a comprehensive picture of business functions.