Data can be traced to various consumer sources. Managing data is one of the most serious challenges faced by organizations today. Organizations are adopting the data lake models because lakes provide raw data that users can use for data experimentation and advanced analytics.

A data lake could be a merging point of new and historical data, thereby drawing correlations across all data using advanced analytics. A data lake can support self-service data practices. This can tap undiscovered business value from various new as well as existing data sources.

Furthermore, a data lake can aid data warehousing, analytics, data integration by modernizing. However, lakes also face hindrances like immature governance, user skills, and security. One among four organizations already has at least one
data lake in production. Another quarter will embrace production in a year. At this rate analyst not only expect this trend to last long but also forecast it to speed up the incorporation of innovative data generating technologies in practice. 79% of users having a lake state that most of the data is raw with some portion for structured data, and those portions will grow as they comprehend the lake better.

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 tiering
concerns.

There are two major types of data lakes based on data platform. Hadoop-based data lakes and relational data lakes.
Hadoop is more usual than relational databases. However, data lake spans both. The platforms may be on premises, on
clouds, or both. Thus, some data lakes are multiplatform as well as hybrid. Though adopting and working on traditional technologies like data mining and data warehousing is important, it is equally important to adopt modern capabilities that not only makes it more evolved but efficient as well. As organizations need to solve challenges at a faster pace, the need has shifted to adopt hybrid methods to explore, discuss and present the data management scenarios. In the present day industries, ideas like data lake to ease data sharing have erupted, but with traditional methods like data warehousing, the scope for growth is limited.

A data lake receives data from multiple sources in an enterprise to store and analyze the raw data in its native
format. In an industry, data lake can handle data ranging from structured data such as demographic data or semi-structured data such as pdf, notes, files to completely unstructured data such as videos and images. Using data
lake, organizations can dive into possibilities yet to be explored by enabling data management technology to avoid
functional shortcomings. With the advancements in data science, artificial intelligence, and machine learning, a data
lake could assist with various efficient working models for this industry, industry-related personnel as well as
specialized capabilities like predictive analysis for future enhancement.

Although data lake is the new face and seems to be in a primitive state, many industry giants like Amazon, Google
etc. have worked on it. They have processed data in a faster and reliable manner creating a balanced value chain. For its
deployment, administration, and maintenance, a lot of efforts has to be instilled. As it is a pool of data from various
organizations, it has to governed, secured and be scalable at the same time to avoid it being a dump of unrelated data
silos.

This white paper will present the opportunities laid down by data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.

Download Enterprise Data Lake: How to Conquer the Data Deluge and Derive Insights that Matter

How to Conquer the Data Deluge and Derive Insights that Matter