Data, and the need to analyze it has been existing since the early days of application building. Conventional datastores (RDBMS, Files) offered the ability to store, extract, and analyze well-structured data in a post-facto manner. The need for analyzing data at the very moment it was processed for business functionality, was never critical. As application ecosystems became complex, distributed, and multi-tenanted in nature, the data came from disparate sources, started losing its ‘structured’ nature and streamed in enormous volumes. Big data/Hadoop systems started replacing conventional data stores.
In today’s world, businesses have to rely heavily on the need to not just process, but also analyze data in real-time. Second, the enormity of data, low-latency and high-throughput requirements have rendered conventional data processing architectures and databases ineffective. Moreover, Actionable Insights – both Operational and Business, are now sought from combinationof: historical, real-time, and predictive/machine learning based analytics.
Additionally, applications need to be enabled for processing enormous data on clusters in a hybrid setup of on-premise and cloud. When enterprises think of hosting an array of applications on cloud, the data persistence often needs a different approach. Conventional data warehouses prove to be both – non-scalable and super expensive. Data Lakes are needed in such scenarios.
With over a decade of expertise in applications space, GS Lab has enabled enterprises to immensely benefit from their own data by building actionable insights and visualizations that have allowed the enterprises to seek Operational and Business insights – both in real time and post facto. We have done so by creating setups that use the modern, state of the art techniques built on streaming and big data analytics, and data lakes, as well as leverage their existing investments – conventional datastores, warehouses and applications.