Processing and storing unstructured data for meaningful use is a bit different than using structured data. With exponential data generation and the majority of the data being in unstructured form, data warehouses are insufficient for data processing as they were designed to deal with only structured data. Here, data lakes solutions built on Apache Hadoop deliver promising solutions for data storage, processing, and analysis of unstructured, multi-structured, and structured data.
Advantages of using data lakes built on Apache Hadoop
Apache Hadoop data lakes easily integrate with data warehouses. Similarly, they can be seamlessly used with almost all contemporary databases and analytical tools.
· Scalable architecture: Uses a distributed storage system, which imparts high scalability to the data storage.
· Parallel processing: Allows users to perform analytics using multiple engines such that a huge volume can be processed at a time.
· Lowers TCO: Reduces the data processing cost to a significant extent, lowers deployment time, and time to insights while using multi-variate data.
· Unified data storage: Offers a unified platform to store unstructured, multi-structured, and structured data.
· Complex analytics: Allows to compute complex queries at high speed by using different analytics tools allowing fast time to insights and hence faster time to market.
Best practices for Apache Hadoop implementation
1.Apache Hadoop configuration: Appropriately configure the storage architecture to aggregate and store varying volumes of multivariate data.
2.Big Data Analytics configuration: Use components that support Big Data Analytics, data encryption, complex querying, and analysis.
3.Elastic search mechanism: Use components for facilitating elastic search and easy retrieval.
4.Data movement: Use components that support the seamless movement of high amounts of data in its original format from different sources into a unified single storage unit.
5.Indexing data: Index the stored data for easy understanding, search, and retrieval of high volumes of data streaming from multiple sources, including apps, social media, IoT devices, etc.
6.Apache Hadoop Big Data framework: Use the framework to perform comprehensive analytics without moving it to a different Analytics system.
7.Machine Learning (ML): Use ML algorithms to generate deeper insights and allow the algorithms to get intelligent with time through exception handling.
8.Business Intelligence (BI) tools: Integrate BI tool with the Apache Hadoop platform to generate faster analytics and visual dashboards and is easily accessible from any mobile device as well as browser.
Data Lakes significantly simplify the challenges related to the analytics of unstructured data. As business requirements evolve constantly over a period of time, businesses need to keep on augmenting their IT architecture to accommodate them. Apache Hadoop platform allows businesses to build data lakes as extensions to their earlier infrastructure and gain business insights from multi-variate data.