AWS Data Lakehouse: A Comprehensive Guide

An AWS data lakehouse is a centralized repository that stores and manages structured and unstructured data in a single location. It combines the benefits of a data warehouse and a data lake.

Data is stored in its raw, natural state, making it easier to analyze and gain insights. This is because data is not transformed or processed before storage, unlike in a data warehouse.

The AWS data lakehouse is built on top of Amazon S3, which provides a scalable and durable storage solution. This allows for easy integration with other AWS services.

With an AWS data lakehouse, you can store and manage large amounts of data from various sources, including logs, sensor data, and social media feeds.

A Data Lakehouse is a new form of data architecture that combines the benefits of Data Lakes and Data Warehouses.

Its flexibility and low cost make it an attractive option for storing large amounts of data, while its ability to handle ACID transactions makes it suitable for business intelligence and analytics.

Data Lakehouses connect the Data Lake, Data Warehouse, and purpose-built databases into a single structure, with a unified Governance approach and tools to move data efficiently.

This unified system enables data teams to work faster and access all their data in one place, without having to navigate multiple systems.

Data Lakes are all about flexibility and scalability, allowing you to store any type of data in its raw form without having to structure it first.

They're also relatively low-cost and can handle large volumes of data, making them a great option for storing and processing big data.

One of the key benefits of Data Lakes is that they can store data in its original format, making it easier to analyze and process later on.

Data Lakes are often used for storing large amounts of unstructured data, such as images, videos, and social media posts.

A Data Lake can be thought of as a central repository for all your data, making it easier to access and analyze across different teams and departments.

Data Lakes are typically used in conjunction with other data storage systems, such as Data Warehouses, to provide a more comprehensive view of your data.

By combining Data Lakes with Data Warehouses, you can create a powerful data architecture that enables fast and agile business intelligence, analytics, and machine learning.

A Data Lakehouse combines the best of Data Lakes and Data Warehouses, offering flexibility, low cost, and scale, along with powerful data management and ACID transactionality.

It connects the Data Lake, Data Warehouse, and purpose-built databases and services into a single structure, with a unified Governance approach, tools, and strategies.

A Data Lakehouse is enabled by a new, open system design that implements data structures and data management features similar to those in a Data Warehouse, directly onto low-cost storage used for Data Lakes.

This merged system allows data teams to move faster while using data without accessing multiple systems.

Data Lakehouses ensure teams have the most complete and up-to-date data for data science, machine learning, and business analytics projects.

AWS Data Lakehouse architecture is designed to simplify the process of setting up a Data Lakehouse. Our experts can help you design and implement Data Lakehouse architectures on both AWS and Azure.

AWS Lake Formation is a service that simplifies the process of setting up Data Lakehouses, allowing organizations to ingest data from various sources and transform it into formats suitable for analytics. This service is a great option for those looking to streamline their data processing workflow.

Here are some key features of a well-designed AWS Data Lakehouse architecture:

By leveraging these features, you can create a robust and scalable Data Lakehouse that meets the needs of your organization.

Our experts excel in designing and implementing Data Lakehouse architectures on both AWS and Azure, ensuring seamless integration with your existing systems. We can help you set up a data lake today without the hassle of configuring storage, moving data, and adding metadata.

Data Lakehouse architectures are structured along swim lanes, including Source, Ingest, Transform, Query, and Process, Serve, Analysis, and Storage. This ensures a clear and organized approach to data management.

Here's a breakdown of the Source swim lane:

Our Data Lakehouse solutions use AWS-specific services for Ingest, Storage, Serve, and Analysis/Output. These services include Amazon Redshift, Amazon AppFlow, AWS Glue, and Amazon S3.

In our architecture, we're leveraging the resources of AWS Glue to create tables inside the AWS Glue Data Catalog by receiving the schema definition from AWS Glue Schema Registry.

The tables are available inside the database wp_trusted_redshift by receiving its definition from the schema registry.

We can see every cluster detail by looking at the console after provisioning our Redshift Cluster.

The JDBC or ODBC URL provided can be used to connect from some external SQL client, but for this scenario, we'll use the Query Editor directly from the Management Console.

To process the data in the trusted zone, we fetch it from the RAW zone, parse it by applying a new schema with the right data types for each column, and write it in parquet file format.

Ingestion is a crucial step in setting up an AWS Data Lakehouse. The Tickit data provided by AWS is already available inside the RAW zone, where it was manually stored.

Each table has its own format and uses a custom separator of columns. To make data ingestion easier, AWS Lake Formation simplifies the process of setting up Data Lakehouses.

This service allows organizations to ingest data from various sources and transform it into formats suitable for analytics. Amazon S3 serves as the foundation for AWS Data Lakehouses architecture.

As a highly scalable and cost-effective storage solution, Amazon S3 can store both structured and unstructured data, providing the flexibility needed for data analytics. However, S3 may lack some metadata structure required for more advanced data management tasks if not paired with Glue or another metastore/catalog solution.

AWS Lake Formation offers an alternative for data teams looking for a more structured data lake or data lakehouse solution.

Data is readily available for analytics and reporting in an AWS Data Lakehouse, empowering data-driven decision-making.

With AWS Data Lakehouses, you can use Databricks SQL as the engine for both serverless and non-serverless analytics, and leverage Unity Catalog for data discovery, exploration, lineage, and access control.

Business analysts can use dashboards, the Databricks SQL editor, or specific BI tools like Tableau or Amazon QuickSight for BI use cases.

AWS provides a range of analytics tools, including Amazon Redshift and Amazon Athena, to derive insights from your Data Lakehouse.

Azure Data Lakehouses also integrate with Azure services like Azure Synapse Analytics to support data analytics and reporting needs.

AWS provides a range of analytics tools such as Amazon Redshift and Amazon Athena, enabling organizations to derive insights from their Data Lakehouses.

These tools are designed to help businesses make data-driven decisions, and with AWS, you can easily integrate them into your existing infrastructure.

Amazon Redshift is a fully managed data warehouse service that allows for fast and efficient querying of large datasets.

Azure Data Lakehouses easily integrate with various Azure services, including Azure Synapse Analytics, to support data analytics and reporting needs.

With these integrations, you can leverage the strengths of both services to create a seamless data analytics experience.

Amazon Athena is a serverless query service that enables you to analyze data in Amazon S3 using standard SQL, without the need for a separate database.

Our team collaborates closely with your organization to develop a Data Lakehouse strategy that's tailored to your unique goals and industry-specific needs. This ensures that your Data Lakehouse is aligned with your business objectives.

Our experts excel in designing and implementing Data Lakehouse architectures on both AWS and Azure, ensuring seamless integration with your existing systems. They can handle complex architectures with ease, giving you a scalable and efficient data management solution.

Developing a strategy for your data implementation is a crucial step in ensuring its success. We collaborate closely with your organization to develop a Data Lakehouse strategy aligned with your unique goals and industry-specific needs.

A well-thought-out strategy will help you navigate the complexities of data implementation and ensure that your data solutions meet your business objectives.

To implement a data lake based on real-time streaming data, we'll use a Kinesis Data Firehose Delivery Stream to feed the data into S3 seamlessly. This approach is more elegant than directly implementing a Kinesis consumer.

A Kinesis Data Firehose Delivery Stream allows us to store consumed events into S3 using the AWS API or SDK, making it a convenient solution for our needs. We'll design the architecture around this stream to ensure smooth data flow.

The proposed solution's architecture design will be illustrated in a diagram, showing the seamless integration of Kinesis Data Firehose with S3. This visual representation will help us understand the data flow and identify potential bottlenecks.

By using a Kinesis Data Firehose Delivery Stream, we can simplify the data processing and storage process, making it easier to manage and scale our data lake as needed.

In the enterprise setting, data sharing is a crucial aspect of collaboration and innovation. Enterprise-grade data sharing is provided by Delta Sharing, which offers direct access to data in the object store secured by Unity Catalog.

Delta Sharing is a powerful tool that enables seamless data exchange between organizations. This is especially useful for businesses that need to share sensitive information with partners or subsidiaries.

Databricks Marketplace is an open forum for exchanging data products. This platform allows businesses to discover, purchase, and integrate data products from various providers.

By leveraging Delta Sharing and Databricks Marketplace, enterprises can streamline their data sharing processes and unlock new insights and opportunities.

Building your data lake with quality in mind is crucial for making informed decisions. This means evaluating data lake vendors on their data management and integration capabilities.

A data lake on the "lakehouse" side of the spectrum offers flexibility by integrating with governance, data quality, and other modern data stack solutions. This is critical for success today.

Data lakes with query logs that can be accessed by Monte Carlo enable end-to-end data observability. This allows you to detect, resolve, and prevent data anomalies.

Maintaining data quality is essential for making well-informed decisions and gaining valuable insights from your data lake. Data observability uses automated monitoring, root cause analysis, data lineage, and data health insights to achieve this.

To bring data quality to your chosen data lake vendor, you can request a demo of the data observability platform.

The Databricks lakehouse offers two reference architectures: a generic one and an AWS-specific one.

The generic reference architecture is structured along five swim lanes: Source, Ingest, Transform, Query and Process, Serve, Analysis, and Storage.

The Databricks lakehouse uses its engines Apache Spark and Photon for all transformations and queries.

Data can be ingested into the lakehouse via batch or streaming, and data is typically stored in the cloud storage system where the ETL pipelines use the medallion architecture to store data in a curated way as Delta files/tables.

The Databricks lakehouse provides Databricks SQL, the data warehouse powered by SQL warehouses, and serverless SQL warehouses for DWH and BI use cases.

Here's an overview of the five swim lanes in the generic reference architecture:

The AWS reference architecture is derived from the generic reference architecture by adding AWS-specific services for the Source, Ingest, Serve, Analysis, and Storage elements.

This view of the reference architecture focuses only on AWS services and the Databricks lakehouse, and the cloud provider services shown are not exhaustive.

The new paradigm of the Data Lakehouse Architecture is arriving to deliver more opportunities to businesses, where now the ranges of technology, frameworks, and cost related to Cloud Platform are more attractive than ever.

This is especially true for businesses planning to start their Data-Driven Journey, where the range of technology, frameworks, and cost related to the Cloud Platform is more attractive than ever.

Data can be received and processed in real-time using Kinesis Data Streams and Kinesis Data Firehose, and delivered to object storage for further usage by Data Analysts and ML Engineers.

Amazon Athena allows for running SQL AdHoc query on the data stored in object storage, making it a valuable tool for data analysis and exploration.