Data can be traced from various consumer sources. Managing data is one of the most serious challenges faced by organizations today. The storage systems need to be managed individually, thus, making infrastructure and processes more complex to operate and expensive to maintain. In addition to storage challenges, organizations also face many complex issues such as limited scalability, storage inadequacies, storage migrations, high operational costs, rising management complication and storage tiring concerns. Organizations are adopting the data lake models because lakes provide raw data that users can use for data experimentation and advanced analytics.

The Need of Data Lake

A data lake is a centralized data repository that can store a multitude of data ranging from structured or semi-structured data to completely unstructured data. Enterprise leaders see the benefit of embracing data lakes into their systems. Enterprises are evolving from their legacy IT environment.
Enterprises could address characteristics as follows to help them embrace Data Lake and gain insight and value from it accelerating their investments.
1. Rate of Investment and legacy technology: With huge data masses, enterprises need to invest in a system to democratize the data within and around it. With the right technology, an enterprise data lake could lead to the success of digital transformation and data-centric models accelerating their investments and returns.
2. Big Data complicacies: Adoption of Data lake is legacy technology being aided by Data lake. Many dynamic elements need to be laced together building the software platforms and solutions that can work congenially.
3. Constant: Enterprise adaptability to sync with the ever occurring changes and pace along so that the data lake remains elusive.
4. Wide range of tools or technologies: There are plethora of tools available which creates confusion ranging from the data lake definition to the right technology for a use case.

A data lake has flat architecture to store data and schema-on-read access across huge amounts of information that can be accessed rapidly. The lake provides fluid data management fulfilling the requirements of an industry as they try to rapidly analyze huge volumes of data from a wide range of formats and extensive sources in real-time. It helps skilled data scientists to draw insights on data patterns, disease trends, data abuse, insurance fraud risk, cost, and improved outcomes and engagement and many more.
Having understood the need for the data lake and the business/ technology context of its evolution, important benefits such as scalability, high Velocity data, Structure, Storage, Schema, SQL, Advanced Algorithms, Administrative Resources annul the challenges.
Data lake supports multiple reporting tools and has self sufficient capacity. It helps elevate the performance as it can traverse huge new datasets without heavy modeling –Flexibility. It supports advanced analytics like predictive analytics and text analytics. This further allows users to process the data to track history to maintain data compliance – Quality. Data lakes allows users to search and experiment on plethora of data from variable sources from one secure view point – Findability and Timeliness.
The data lake with Lambda Architecture’s aid, works with the CAP theorem on a contextual basis. As per researchers, experts and data enthusiasts, the “Data Lake” to “a successful Data and Analytics” transformation needs the Data and Analytics as a Service (DAaaS) model.
Presently, data lake practices are governed by Hadoop predominantly. Hadoop has become the major tool for assimilating and pulling out insights from combinatorial unstructured data present in Hadoop and enterprise data assets, running algorithms in batch mode using the MapReduce paradigm. Hadoop, with the existing enterprise data assets such as data in mainframes and data warehouses. Languages such as Pig, Java, MapReduce, SQL variants, R, Hadoop, Apache Spark, and Python are being increasingly used for data munging, data integration, data cleansing, and running distributed analytics algorithms.
Technology should be able to let organizations acquire, store, combine, and enrich huge volumes of unstructured and structured data in raw format and have the potential to perform analytics on these huge data in an iterative way. Data lake may not be a complete shift but rather an additional method to aid the existing methods like big data, data warehouse etc. to produce tremendous value as a component of an enterprise’s data strategy mining all of the scattered data across a multitude of sources opening new gateway to new insights keeping cloud in mind.
There is more to consider with details including: big data architecture for accessible data lake infrastructure, data lake functionality, solving data accessibility and integration at enterprise level, data flows in the data lake, and many more. With these numerous queries, there still is resources to tap and a lot to gain for the enterprise. Using the data lake architecture to derive cost efficient, life-changing insights from the huge mass of data nullifies the concern regarding going further with the ice-berg hidden under the ocean.