In the digital world, IoT and wearables are resulting in a data explosion and an upsurge of different types of data have put pressure on data systems across the organization. Many organizations are struggling to manage volumes of expanding data breadth and are challenged by the constrictions of outdated legacy systems resulting in an inadequate storage environment. Limited capacity leaves businesses with little room for storing data securely. As the volume of big grows daily, the question arises: How can all data be stored in an organized manner so that is can be secured and accessed readily by the organization. This big promise cannot take place with antiquated legacy systems incapable of communicating across the network.
Simply installing new systems deprives the organization of all the valuable historical insights in the previously collected data. This data has analytics potential and value and should be retained. To attain this, organizations need to scale their storage environment. When it comes to what direction data storage will take, it is often a data lake option that enables the digital transformation. A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data. The data structure and requirements are not defined until the data is needed.
Initiatives for making the enterprise data useful utilize data lake for the transition into actionable insights. Organizations that are compelled to use a myriad of data to respond to competitive and regulatory pressures have begun adopting data lakes to manage their historical data. Such an approach elevates the need to discard legacy platforms. Data Lakes are slowly emerging as a powerful weapon for tackling data integration challenges as enterprises increase their exposure to mobile and cloud-based applications. Hypothetically, access to petabytes of data needs to be closely monitored to maintain any level of security and safety. Moving from cloud-based data archiving to a data lake is the foremost step in making your data actionable. Having a data lake in an enterprise offers them the ability to store disparate records in native formats until they are ready for analyzing at a later date. This is much more efficient that trying to integrate found in data warehousing scenarios.
Data lakes remove integration barriers and provide a platform for more informed real-time business decisions. They offer a means of enriching, creating and managing metadata which is incrementally essential. Moving from cloud-based data into data lakes helps organizations to integrate and transform unstructured and structured data in real-time, thus proving actionable Business Intelligence (BI) that provides answers to complex questions. It helps to resolve the nagging problem faced by organizations for data accessibility and integration. Enterprises need to be able to manage growing data volume to plan for future demands for storage. Data lakes are a refined approach for data management helping organization to increase their productivity by capturing, accessing and analyzing data from different sources. Their usage in organizations enables agile BI, thus offering the ability to improve the business outcomes of the consumed data in real-time. This helps in the delivery of the right results to the right user at right time. Data Lakes are a simple yet powerful approach to solving business problems as they cater to ever- changing business needs by allowing for storage of all data and offering the capability of deriving actionable inputs in an enterprise-wide environment. It assures to improve the delivery speed of information and inputs into the business without the troubles imposed by IT-centric data warehousing processes. Irrespective of the method of use, organizations need to take some time to see
Data Lakes are a simple yet powerful approach to solving business problems as they cater to ever- changing business needs by allowing for storage of all data and offering the capability of deriving actionable inputs in an enterprise-wide environment. They assure improved delivery speed of information and inputs into the business without the difficulties imposed by IT-centric data warehousing processes.
Irrespective of the method of use, organizations need to plan for future data demands relating across the enterprise. As business is increasingly becoming digital, providing access to data is becoming a critical element in the speed of deployment and development new products. The Data lake is a platform that can host those unknown demands. When the data is freed, enterprises have the capacity to innovate like never before. Implementing a data lake approach is a critical way to differentiate the enterprise from its competitors by deploying an open data approach.
Scalable Systems MISSISSIPPI platform can address the challenges of data storage, integration, and accessibility for organizations. Based on Hadoop architecture, MISSISSIPPI the data lake platform by Scalable Systems introduced as a platform for delivering deep insight across a large, broad, diverse set of data at efficient scale in a way that existing enterprise systems and tools can integrate with. It provides the core capabilities to parse, store, manage and query massive data sets, enabling organizations to focus their effort on building their bespoke Big Data solutions.