**This
is a sponsored post which I will be compensated for**
A data catalog
is essential to business users because it synthesizes all the
details about an organization's data assets
across multiple data dictionaries
by organizing them into a simple, easy-to-digest format.
Thus,
an essential component of an Amazon S3-based data lake is
the data catalog. The data catalog provides a query-able interface of all assets stored in the data lake's S3 buckets.
The data catalog is designed to provide a single source of truth about
the contents of the data lake.
Any
entity that is comprised of data a data asset may be a system or
application output file, database, document, or web page. A data
asset also includes a service that may be provided to
access data from an application. For example, a service that returns
individual records from a database would be a data asset.
Data catalog
tools helps dramatically
improves the productivity of analysts, increases the accuracy of analytics, and
drive confident data-driven decision-making while empowering everyone in your
organization to find, understand, and govern data.
Data catalogs are soaring as organizations continue to
struggle with finding, inventorying, and analyzing vastly distributed and
diverse data assets. Data and analytics leaders must investigate and adopt
ML-augmented data catalogs as part of their overall data management solutions
strategy.
The benefits of data catalog tools are
easier, faster search capabilities: These lead to better, more accurate,
reliable analytic assets. Businesspeople can quickly find their needed
information and get better business insights. This leads to more trust in the
environment and therefore, higher adoption rates of analytic assets. This also
enables first-time and future users to ramp up quickly.
Better
compliance with internal policies, like security and privacy, and external
regulations, like GDPR and CCPA: AI and ML capabilities can detect “sensitive”
data like HIPAA or PII fields while usage tracking can determine potential
access or illegal usage patterns and create an audit trail for compliance.
Cost
savings: A significant amount of effort and time is spent creating analytical
assets that already exist. Business users often reinvent the wheel when they
can’t easily find a report. Data scientists create redundant data sets when
their existence is not readily obvious. The inability to find what you want
causes wasted time and effort in addition to redundancy of data and data
assets. These all cost significant amounts of money. A data catalog highlights
redundancy and inconsistencies and supports streamlining this overly complex
environment.
Collaboration
and annotation features: Context is critical to understanding and trusting
these assets, making collaboration and annotation significant capabilities in
data catalogs. These provide mechanisms for business people to determine if the
analytical asset is appropriate for their needs, to find like-minded people
creating assets they need, to form collaborative units with these like-minded
people.
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