Data Lake Catalog Configuration and Governance

A data lake catalog is a centralized repository that stores metadata about the data in your data lake. This metadata includes information about the data's structure, content, and relationships.

To configure a data lake catalog, you need to define its metadata schema, which includes the types of metadata you want to store and the structure of that metadata. For example, you might include information about the data's source, format, and processing history.

A well-configured data lake catalog provides a single source of truth for your metadata, making it easier to govern and manage your data. This is especially important in large-scale data environments where data is created and modified constantly.

Data governance in a data lake catalog involves setting policies and procedures for managing metadata, such as data quality checks and access controls.

A data lake catalog is a centralized location that stores information about all the assets in your data lake. It's like a map that helps you find what you need.

The AWS Lake Formation catalog provides a query-able interface of all assets stored in the data lake's S3 buckets. This makes it easy to discover assets, manage metadata, and define consistent access control policies for all its consumers.

Organizing your catalog databases by source of data is a good practice. This helps you keep track of where your data is coming from and makes it easier to manage.

Here are some key components of a data lake catalog:

A data catalog provides a single source of truth for the contents of a data lake. It's an essential tool for organizations that want to build a central data catalog to make it easy for users to discover datasets, enrich them with metadata, and control access.

To set up a data lake catalog, you'll need to associate it with an object storage account, which requires a metastore service. This is a crucial step before you can query data in your object storage account.

Before you start, make sure to read up on object storage and the requirement for a metastore in the Using object storage systems documentation.

To add your catalog to an existing cluster or create a new one, follow these steps:

To configure your metastore, you'll need to associate it with your object storage account. This is a necessary step before you can query data in your object storage account.

You can use a Hive Metastore Service (HMS) to manage the metadata for your object storage, and it must be located in the same cloud provider and region as the object storage itself.

To establish a connection to the HMS, you can either allow the Starburst Galaxy IP range to connect directly or use an SSH tunnel with a bastion host in the VPC.

To configure access, you'll need to specify the Hive Metastore host, Hive Metastore port, Allow creating external tables, and Allow writing to external tables.

Here are the parameters you'll need to configure:

Alternatively, you can use Starburst Galaxy's built-in metastore service, which provides a convenient and easy-to-use solution for managing your metadata.

When specifying the default table format for an object storage catalog, you have four options to choose from: Iceberg, Delta Lake, Hive, and Hudi. These formats are supported by Starburst Galaxy, and they'll apply to newly created tables.

The default format is Iceberg, which is a good choice if you're unsure. You can find more information about the format options on the Storage page.

The default table format is not applied to existing tables, so you won't need to worry about converting any of your current data.

Here are the supported table formats:

SQL support is available for your object storage catalog, but the level of support depends on the table format in use.

You can find more details on SQL statement support for Azure Data Lake Storage catalogs on the storage and table formats pages.

To determine the level of SQL support for your catalog, check the table format you're using.

AWS Lake Formation is a powerful tool for managing and organizing your data. It's hosted in multiple locations worldwide, known as AWS Regions and Availability Zones.

Each AWS Region is a separate geographic area, providing a secure and reliable environment for your data.

AWS Lake Formation is built around the concept of a data lake, which is a schematic and organized representation of your registered corporate data assets stored in Amazon S3.

A data lake in AWS Lake Formation is made up of databases, tables, and columns, providing a clear and structured way to store and manage your data.

AWS Lake Formation also uses a blueprint, which is a data ingestion template designed to easily ingest un-transformed data from various data sources.

This blueprint is particularly useful for ingesting data from relational databases and load balancer logs into Amazon S3 to build a data lake.

A data catalog is also a crucial part of AWS Lake Formation, containing information about all assets that have been ingested into or curated in the S3 data lake.

This data catalog provides an interface for easy discovery of data assets, security control, and a single source of truth for the contents of a data lake.

Managing a data lake catalog requires a structured approach to ensure data quality and governance. A good data catalog, backed by data flow discovery, can identify flows between disparate datasets and help you discover data movement within your organization that may not be well-known.

To effectively manage a data lake catalog, it's essential to organize catalog databases by source of data. This helps in easy discovery of assets, managing metadata, and defining consistent access control policies for all consumers. A data lake catalog contains information about all assets that have been ingested into or curated in the S3 data lake, providing a single source of truth for the contents of a data lake.

Here are some key considerations for data lake catalog management:

By following these best practices, you can ensure that your data lake catalog is well-managed, and your data is easily discoverable and accessible to all stakeholders.

Unstructured data is the data that doesn't conform to a data model and has no easily identifiable structure. This can include documents, web pages, email, social media content, mobile data, images, audio, and video.

It's not a good fit for a mainstream relational database, but your data catalog can help make implicit data structures explicit. This can be achieved by re-designing the overall data structure based on the team or organizational requirement.

For example, Unity Catalog, an open-source initiative by Databricks, provides a universal catalog that manages data and AI assets across various clouds, data formats, and platforms. It supports a wide range of data formats and processing engines, including Delta Lake, Apache Iceberg, Apache Parquet, CSV, and more.

Considering unstructured data can be vital for any data catalog. It's essential to process metadata, which describes the circumstances of the data asset's creation and when, how, and by whom it has been accessed, used, updated, or changed.

This can help an analyst decide if the asset is recent enough for the task at hand, if it comes from a reliable source, if it has been updated by trustworthy individuals, and so on. Process metadata can also be used to troubleshoot queries.

Here are some examples of how to consider unstructured data in your data catalog:

By considering unstructured data and processing metadata, you can create a robust and comprehensive data catalog that meets the needs of your organization.

Managing data flows is a crucial aspect of data lake catalog management. By identifying and tracking data flows between disparate datasets, you can discover hidden relationships and ensure data integrity.

Data lineage and provenance tools are a good start, but they often focus on a specific domain or set of domains. A good data catalog, backed by data flow discovery, will reveal flows between datasets that may not be well-known.

Data flows can be complex, involving multiple sources, transformations, and destinations. To manage data flows effectively, you need a robust data catalog that can capture data lineage, track data transformations, and identify data relationships.

Here are some key considerations for managing data flows:

By managing data flows effectively, you can ensure data quality, reduce data errors, and improve data insights. A good data catalog will provide a single source of truth for data flows, enabling you to make informed decisions about data management and governance.

Data governance and security are crucial aspects of a data lake catalog. An effective data catalog helps identify the location of sensitive data, making it easier to manage and protect.

Having a data catalog can minimize the surface area for breaches by identifying and managing sensitive and redundant data. This is especially important in scenarios where the same sensitive data is found in multiple places.

Authentication to ADLS is a crucial step in securing your data. To grant access to your object storage account, you need to select an authentication method.

There are two options to choose from: Azure service principal or Azure access key. You can select a service principal alias from the drop-down list of configured service principals.

If you haven't configured a service principal yet, you can do so by clicking on "Configure an Azure service principal". This will guide you through the process. For more information, refer to the guidance in the article.

Alternatively, you can provide the ABFS access key for the specified storage account. To obtain this access key, follow the instructions in the article.

Here are the two authentication methods listed for your reference:

Setting permissions is a crucial step in data governance and security. It allows you to control who can access and modify your data.

You can assign read-only access to all roles by selecting the Read-only catalog switch. This grants a set of roles read-only access to the catalog's schemas, tables, and views.

To specify the roles that have read-only access, use the drop-down menu in the Role-level permissions section. Click Save access controls to save your changes.

One set of roles can get full read and write access to all schemas, tables, and views in the catalog, while another set of roles gets read-only access. This is known as specifying read-only access and read-write access separately for different sets of roles.

To assign read/write access to some or all roles, simply leave the Read-only catalog switch cleared. This will allow the specified roles to have full read and write access to the catalog.

Here are the steps to assign read-only access and read-write access:

Prioritizing sensitive data is a crucial step in data governance and security. It helps identify the location of sensitive data, which can be found in multiple places.

Having a data catalog can help you manage sensitive and redundant data, minimizing the surface area for breaches. This is especially important when sensitive data is scattered across different locations.

In scenarios where sensitive data is redundant, a data catalog can help you identify and eliminate it, reducing the risk of data breaches.

Data governance and security are crucial components of a well-designed data architecture. A key aspect of this is understanding the differences between data warehouses and data lakehouses.

Data warehouses are designed for structured data and optimized for complex queries and transactions. They provide strong governance, security, and performance but can be expensive and less flexible.

Data lakehouses, on the other hand, offer a more flexible and scalable solution. By merging the strengths of data lakes and warehouses, data lakehouses provide robust data management and governance.

Here's a comparison of data warehouses and data lakehouses:

By understanding these differences, data engineers and architects can make informed decisions about which architecture to use for their specific needs.

In the business world, data governance and security are crucial for protecting sensitive information and maintaining a company's reputation.

Data breaches can be devastating, with the average cost of a breach in the US being over $8 million. Companies like Equifax and Anthem have faced massive financial losses due to data breaches.

Having a robust data governance framework in place can help prevent data breaches. This framework should include clear policies and procedures for data handling, storage, and disposal.

Data encryption is a key component of data security, with 80% of companies using encryption to protect their data. Encryption ensures that even if data is stolen, it's unreadable to unauthorized parties.

Regular data audits can help identify vulnerabilities and weaknesses in a company's data governance framework.