As data continues to grow in both volume and formats across multiple deployments, performing analytics has become more complicated. By 2019, 75% of analytic solutions will incorporate 10 or more external data sources. Organizations able to glean insights from this diverse data set will have competitive advantages, from deeper understanding of customers, better responsiveness to trends, and more efficient operations, to name just a few.
This reality of data diversity has given rise to the “data lake”-a data management architecture that allows organizations store and analyze a wide variety structured and unstructured data.
A data lake is a method of data storage. What makes this approach unique is that all of the data is stored in its native format. This means that data in the lake might include everything from highly structured files to completely unstructured data such as videos, emails and images.
In addition, it is not only IT that is now integrating data. Business users are also getting involved with new self-service data preparation tools. The question is, is this the only way to manage data? Is there another level that we can get reach to allow us to more easily manage and govern data across an increasingly complex data landscape?
This seminar/Conference looks at the challenges faced by companies trying to deal with an exploding number of data sources, collecting data in multiple data stores (Cloud and on-premises), multiple analytical systems and at the requirements to be able to define, govern, manage and share trusted high quality information in a distributed and hybrid computing environment.
It also explores a new approach of how IT data architects, business users and IT developers can collaborate together in building and managing a Logical Data Lake to get control of your data. This includes data ingestion, automated data discovery, data profiling and tagging and publishing data in an information catalog.
It also involves refining raw data to produce Enterprise Data Services that can be published in a catalog available for consumption across your company. We also introduce multiple Data Lake configurations including a centralised Data Lake and a ‘logical’ distributed Data Lake as well as execution of jobs and governance across multiple data stores.
Recent times, globally there is an increasing deployment of data lakes in banking and financial services sector is boosting the growth of the market. Data lake describes any large data pool in which the data requirements are not defined until the data is queried. Data lakes provide various benefits like scalability and can accommodate high speed data. It also offers advanced analytics by utilizing the availability of large quantities of coherent data. Global Data Lakes Market was valued at USD 3.24 billion in 2017, and is expected to reach a value of USD 14.01 billion by 2023 at a CAGR of 27.4%, over the forecast period (2018-2023). Data Lakes have become an economical option for many companies rather than an option for data warehousing. The growing use of IoT in many offices and informal spaces has emphasized in the need for data lakes for quicker and efficient manipulation of data. The data lakes market is segmented by software and services. The services segment is expected to grow at the highest CAGR during the forecast period, out of which data lakes services in the services segment is projected to witness the highest demand due to the growing need of data lake software solutions across organizations. Major business functions for which the data lake is deployed are marketing, sales, operations, finance, and human resources. Organizations are fast deploying data lakes solutions either on their premises or on-demand. The demand for on-demand or cloud-based data lakes market solutions is increasing due to the cost-effective and time-efficient features; its growth is specifically high in SME\'s where low cost solutions are much required.
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