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What is a Data Catalog?Data Catalog provides a single self-service environment to the users, helping them find, understand, and trust the data source. It also helps the users to discover the new data sources, if there are any. Discovering and understanding data sources are the initial steps for registering the sources. Users search for the Data Catalog Tools based on the needs and filter the appropriate results. In Enterprises, Data Lake is needed for Business Intelligence, Data Scientists, ETL Developers where the right data needed. The users use catalog discovery to find the data which fits their needs. Key capabilities of this approach are as follows:
- Search and discovery
- Data Intelligence
- Manage business glossary
- Empowering Confidence in data
What is Meta Data?
Metadata is a thread that connects all other building materials, including ways for ingestion to be aware of sources, refinement to be connected to ingestion, and so on. Every component of the architecture contributes to the development and use of metadata.
Key Ingredients for a Successful Data Catalog
All data catalogs are not created equal. It's critical to filter players based on key capabilities when selecting a data catalog. As a result, many data catalogs, including Talend Data Catalog, depend on critical components that will ensure your data strategy's effectiveness. Let's take a look at some of the essential features:
Connectors and easy-to-curation tools to build your single place of trust
The data catalog's ability to map physical datasets in your dataset, regardless of their origin or source, is enhanced by having many connectors. You can extract metadata from business intelligence software, data integration tools, SQL queries, enterprise apps like Salesforce or SAP, or data modeling tools using powerful capabilities, allowing you to onboard people to verify and certify your datasets for extended use.
Automation to gain speed and agility
Thanks to improved automation, data stewards won't waste time manually linking data sources. They'll then concentrate on what matters most: fixing data quality problems and curating them for the whole company's good. Of course, you'll need the support of stewards to complement automation – to enrich and curate datasets over time.
Powerful search to quickly explore large datasets.
The quest should be multifaceted as the primary component of a catalog, allowing you to assign various criteria to perform an advanced search. Search parameters include names, height, time, owner, and format.
To conduct root cause analysis, use Lineage.
Lineage allows you to link a dashboard to the data it displays. Understanding the relationship between various forms and data sources relies heavily on lineage and relationship exploration. So, if your dashboard shows erroneous data, a steward may use the lineage to determine where the issue is.
Glossary to add business context to your data
The ability to federate people around the data is essential for governance. To do so, they must have a shared understanding of words, definitions, and how to relate them to the data. As a result, the glossary is helpful. If you look for PII in a data catalog, you'll find the following data sources: It's especially useful in the context of GDPR (General Data Protection Regulation), where you need to take stock of all the data you have.
Profiling to avoid polluting your data lake
When linking multiple data sources, data profiling is essential for determining your data quality in terms of completeness, accuracy, timeliness, and consistency. It will save time and enable you to spot inaccuracies quickly, allowing you to warn stewards before polluting the data lake.
How Data Catalog Works?
Building a Catalog starts with collecting the metadata of the sources. After obtaining the metadata, the metadata entities need to categorize and assign different tags. ML (Machine Learning) and NLP (Natural Language Processing) are used to automate these processes. Metadata entities auto-assign a tag according to the name of the data entity with the Machine Learning model help. In the end, the data steward reviews the things and adds more value to Data Catalog.
What are the benefits of Data Catalog?
The below highlighted are the benefits of Data Catalog:
- Spend more time using the data not found - As per Forrester Forbes report, data scientists spent more than 75% of their time understanding and finding the data. And more than 75% of them don't like that part of their job—this due to the questions which they have before working on the queries. The main reason for this problem in an organization is the poor mechanism of handling and tracking all the data. A good Catalog helps the Data Scientist or Business Analyst understand the data and answer the question they have.
- To implement Access Control - When an organization grows, role-based policies are needed, don't want everybody to modify the data. Access Control should be implemented while building the Data Lake. Particular roles are assigned to the users, and according to those roles, Data Access should be controlled. In the Hadoop ecosystem, implement using Apache Ranger. For the sensitive data in the Data Lake, use encryption for the Data Protection.
- To Reduce Cost by Eliminating Data Redundancies - A good Catalog Tool helped us find the data redundancies and eliminate them. This can help us to save storage costs and data management costs.
- To follow Laws - There are different protection laws to follow as per the data, such as GDPR, BASEL, GDSN, HIPAA, and many more. These laws must be followed while dealing with any data. But these laws stand for different use-cases and don't imply every data set, to understand that we need to know about the data set. A good Catalog helps us make sure that Data Compliance's followed by giving a view on Data Lineage and using Access Control.
AWS Data Catalog is a prospering metadata management service nowadays. Click to explore about, AWS Data Catalog
Why Data Catalog Matters?
- Helps in Understanding the data - A good Catalog helps the user in understanding the data. A catalog makes it easier for the user to find the relevant data and know the data; it also gives information about the data, such as where it is being used and where it's generating.
- Allow users to work with multiple data sources - The catalog consists of one or more data sources. It helps users to find quality data sources and to gain better knowledge about multiple data sources.
- To Follow Regulatory compliance - There are several data-related laws like GDPR, BASEL, and HIPAA. All of these laws are driven from different perspectives and use cases, but in the end, they all come down to better governance of data with a focus on Data Lineage and Access Control.
How to Adopt Data Catalog?
Building a Catalog is a multi-step process that includes -
- Metadata Extraction - It is the very first step of building the catalog. In this step, the metadata of a defined source is collected and stored in the metadata store. It helps in understanding the defined data asset.
- Data Sampling - It is used to understand the schema, tables, databases.
- Auto-Titling (ML and NLP) - Every organization has a naming convention of using abbreviations to define the schema. Natural language processes model to assign that abbreviation a common name understood by the users who are using Catalog.
- Query Log Ingestion - It collects additional information about the data sets and gives a complete picture of each data set, just like Data Lineage, Data Usability.
- Crowd Sourcing & Expert Sourcing - Up to this layer, the Catalog is ready and just needs to add more values to the Data Catalog. NLP model has corrected the names of the data assets collected from the data sources but the Computer-Human Collaboration also necessary to verify the things.
HIPAA Stands for Health Insurance Portability and Accountability Act. It is specially designed for the protection of patient informationFrom the Article: What is HIPAA Compliance? | HIPAA Requirements
What are the best practices of Data Catalog?
The best practices for Data catalog are listed below:
- Assigning Ownership for the data set - Ownership of each data set must be defined. There must be a person to whom the user contacts in case of an issue. A good Catalog also must tell about the owner of any particular data set.
- Machine-Human Collaboration - After building a Catalog, the users must verify the data sets to make them more accurate.
- Searchability -The Catalog should support searchability. It enables Data Asset Discovery; data consumers easily find assets that meet their needs.
- Data Protection - Define Access policies to prevent unauthorized data access.
Top Data Catalog software tools
These are some top Data Catalog tools :
1. Alation Data Catalog
2. Alex Augmented Data Catalog
3. Ataccama Data Catalog
4. Atlan Data Discovery & Catalog
5. AWS Glue Data Catalog
6. Collibra Data Catalog
7. Erwin Data Catalog
A Collaborative ApproachUsing Data Catalogs Enterprises can centralize metadata in one spot, and enable a full representation of each part of data across databases and incorporate knowledge regarding the data’s position and statistics; for incorporating this approach, we suggest taking the following steps -
- Learn more about GCP Data Catalog.
- Read more about Azure Data Catalog.