Introduction to Data Catalog and Discovery Platform
In today's data-driven world, companies are increasingly recognizing the value of data and investing more in harnessing its power to drive decision-making and ensure the reliability of critical assets. As organizations build data platforms, organizing, centralized, and easily discoverable data becomes crucial. By cataloging data accurately and ensuring its cleanliness and observability, companies can achieve seamless data ingestion and unleash its full potential.
Top 5 Use Cases of Data Catalog in Enterprises
When Data Discovery Meets Data Catalog?
Data catalogs work best with rigid models, but with the increasing complexity of data pipelines, complex, unstructured data becomes an essential standard for understanding our data. This is where the data discovery platform plays a crucial role by providing a dynamic understanding of data. It shows how data is being ingested, stored, and used by consumers. The data catalog, along with data discovery, provides superior accessibility and real-time understanding of the data. It can map the difference between the current state and the ideal state.
Data Catalog + Data Discovery = Data Catalog 2.0
Explore Top Enterprise Data Catalog Tools
Questions we ask in the data discovery process
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What is the most recent dataset? Can any data sets be deprecated?
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When was any table updated?
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What is the meaning of a given field in my domain?
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Who is authorized to access this data? When was this data used? Who used this data?
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What are the upstream and downstream uses of this data?
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Is data passing environmental quality benchmarks?
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What data matters for my business requirements?
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What are my assumptions about this data, and are they being met?
A good Catalog helps the user in understanding the data. Click to explore about, Guide to Data Catalog Tools and Architecture
How to select the Data Catalog Platform you need?
What platform to select can be answered by a simple question about how we help users find the data they need and how secure it is. Some of the ways are:
1. Find data by Search
All the platforms allow users to search for table names based on keywords. Some platforms enhance their approach by giving extensive tables and user descriptions. Once discovered, the data can be reverted based on popularity.
2. Find data by Recommendations
Recommendation-based tables are one of the ways to provide the given data for the table. This may act as a homepage. The recommendation can be based on popular tables within a team/organization or recently used tables or by providing the most queried data by the current user.
3. Find data by Free Text
Search terms can be parsed on the basis of a spacy-based library. Then, a table of candidates can be generated based on data graphs, and elected candidates can be ranked based on users. All this together can give the ability to parse data with Natural Language Queries.
Importance of Data Catalog and Data Discovery Platform
The need for a Data Catalog and Data Discovery Platform is mentioned below:
1. Asset Collection
Users can logically catalog different kinds of data across platforms in various collections to support business definitions and use cases. The platforms make it easier to classify, search, and share corporate knowledge, thereby increasing the efficiency of finding the right, which provides insights to address business challenges.
2. Data Profiling
The data catalog and discovery platform helps you understand the real meaning of your data and its meaning, origin, or point of ingestion. Data profiling helps analyze key metrics and classify sensitive personal information, resulting in better insights and classified-based protection, thus answering questions like What is the most recent dataset? Can any data sets be deprecated?
3. Lineage and Impact
Increases data reliability by encapsulating the origin and accentuating the process that created it, thus giving a transparent process by showing how data is being used, how it travels through the pipeline, and the impact on downstream tables, answering questions like Who is authorized to access this data? When was this data used? Who used this data? What are the upstream and downstream jobs of this data?
4. Security and Classification
Data assets can be organized into categories and curated for faster and easier discovery this can also help in advanced security and governance. Automation ensures that compliance rules are applied consistently at the derived data set. This, in return, answers questions like Who is authorized to access this data? When was this data used? Who used this data?
5. Audit and Monitoring
Dashboards and metrics provide insights into data usage. The insights can provide access patterns and trends and help direct alerts where needed. They can also alert data stakeholders to potential unauthorized access/usage of data.
6. Business Glossary
View your business data and help get the right information to users using natural language queries. We can give business meaning to the data set by categorizing terms from hierarchically glossary vocabulary.
Conclusion of Data Catalog with Data Discovery
In conclusion, Data discovery and cataloging enlighten us about what we have and how it relates. Together, they discover relationships between different data sets and help us connect the dots between them. They provide a reservoir of information about data assets, what they contain, where they are most relevant, and who might have access to them. Both jointly act as vocabulary for the business logic for the data.
