While the term Data is in cognizance of business of all sizes even the most layman person is aware of the buzz and fuss around Data. So from Database to Data Warehouse and now this Data Lake, we have come a long way. What is a Data Lake? Simplistically, is a Architecture approach that can store unstructured and semi-structured data in a single repository and can later be used for multiple use cases from deriving , to and even for building models. Data Lake Big Data insights Stream analytics Machine learning Getting the house in order When a business invests in Data analytics, they just know about the business goals to be achieved using the existing. Sustainability is the biggest miss when the decisions are made haphazardly in excitement to utilize the available data which results in bigger chaos with more cash burning into these modular initiatives. To remedy this, a systematic approach of adopting data lake architecture can bring a huge advantage to all sizes of companies, in a data lake you can store all sorts of data for multiple initiatives from multiple sources. e.g. – You recently built a data mart intended to provide insights using a star schema for the accounts department to track and optimize expenses or you have created stream analytics with a visualization dashboard to monitor real-time activity of traffic on the news section of a website. You will have to invest separately if you plan another such move, however with a data lake this process can achieve all applicability in a monolithic data lake architecture. The Evolution towards comes from Databases to Databases ultimately leading up to Data Lakes. Data Lake SQL NoSQL Access to huge sums of Data Data from all sources be it sensors, logs, social media, web activity or Ads and even internal logs everything can be dumped into a data lake and later can be used for a variety of purposes. Data required for batch processing or real-time stream analytics seems reasonably sensible to first and then do analytics or AI upon the data. store everything For instance, if you can derive more insights ( ) than your competitor, it gives you a lead in the market. based on social media, market trends, customer feedback Breaking beyond silos When there is a common dump for data across all business units it becomes fairly smooth to access data beyond silos and utilize it to derive insights that are not possible with conventional analytics methods. Even data collected across multiple units and divisions can generate insights that could benefit the overall operational hierarchy. Data Lakes won’t evaporate your data ever In the age of Data adopting a sustainable and long-term implementation, the strategy should be a business’s first priority so a foundation can be laid for upcoming decades. It can accommodate the needs of the most advanced implements to enable organizations to become fully data-driven. Footnotes In the end, it is convincingly apparent that data lakes aren’t going away anytime soon in fact they are the future of Data Modernization when a business aims to achieve manifold benefits of Analytics, Machine learning, and advanced AI.

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