A huge part of big data analytics is about aggregating data and identifying meaningful facts from the data. Anything from reports consolidated over a period of time, like sales reports or purchase reports to human efficiency reports can be easily consolidated by running queries across terabytes or even petabytes of data.
When there are huge amounts of data collected from multiple sources, it is almost always possible that there are meaningful trends in data. Using Hadoop, we can run scripts that goes over these wide amounts of data while optimally using the hardware resources to recover meaningful information from this unstructured raw data.
In many industry verticals like manufacturing and insurance, the historical data collected over a time in the past often provides us with valuable information on the outcome of certain events. Predictive Metrics are facts that are found by uncovering these past trends by identifying recurring anomalies in the data. These type of information saves a lot of money for these industries.
Production defects on enterprise software, if left unfound can cause a lot of damage, both financially and operation wise for an industry. Big Data analysis helps in identifying these defects by analyzing incoming log data in real-time or often near real-time. The advances in technologies like Spark helps us comb through several gigabytes of data in near real-time which in turn helps in identifying issues that are critical in production.
Huge software applications generate tremendous amounts of logs that will often contain several valuable information related to events that occur during the execution. Warnings and Errors generated in these logs may hamper performance and it is vital to identify these and ﬁx them appropriately. Hadoop with tools such as Flume and Sqoop helps us to process these logs effectively.
Hadoop with the power of Hive and Spark SQL have recently gained prominence as open source alternatives to run queries often SQL like queries, over huge amounts of structured data as opposed to using paid and premium tools like Vertica and Teradata etc.