The concept of a Databricks lakehouse has gained prominence in recent times. A lake house combines the best of data warehousing and data bricks to create a unified and versatile data ecosystem. Migrating from a data warehouse involves simple steps.
Azure Databricks, powered by Apache Spark and Delta Lake, provides a solid platform for migrating your data warehouse workloads to a modern lake house architecture. This transformation is about more than just moving data. It’s a strategic shift towards:
- Simplifying data operations
- Improving data quality
- Improving analytics capabilities.
This article will discuss the factors, recommendations, and essential steps in moving your warehouse to the Lakehouse. This change can give your company a more effective and consistent data platform from which data engineers, scientists, and analysts can easily interact and derive insightful information.
Concepts to Get Before Migrating to Databricks Lakehouse
Migrating your Enterprise Data Warehouse (EDW) to the Lakehouse is a complex switch but a strategic data infrastructure migration.
It involves the following key steps and considerations:
1. Minimal Code Refactoring
The good news is that once you’ve finished the first data migration and governance settings, most of your existing workloads, queries, and dashboards created for your EDW can run with little to no code reworking.
This ensures a smooth transition and minimizes disruptions to your ongoing analytics processes.
2. Unification of Data Ecosystem
Your data ecosystem needs to be unified, which is the primary motivation behind moving to the Lakehouse. The goal is to improve and simplify data warehousing rather than to do away with it altogether. By doing this, you give data scientists, engineers, and analysts access to the same data tables on the same platform, lowering complexity, upkeep requirements, and total cost of ownership.
3. Data Extraction, Transformation, and Loading (ETL)
ETL activities are a strength of Apache Spark, the foundation of Databricks. Your data professionals can work with the same data in the same environment if your EDW is replaced with a lakehouse. Your organization’s time to insight is sped up, dependencies are decreased, and data pipelines are made more straightforward.
Factors to Keep in Mind Before Loading Data into the Lakehouse
Azure Databricks provides a wealth of tools and capabilities to facilitate data migration to the lakehouse and configure ETL jobs. Let’s explore some of these tools and options:
- Shifting a Parquet Data Lake to Delta Lake: Delta Lake, the transactional storage layer for Databricks, offers ACID (Atomicity, Consistency, Isolation, Durability) guarantees and improved data quality. Learn how to migrate your Parquet data lake to Delta Lake seamlessly.
- Run Queries Using Lakehouse Federation: Lakehouse Federation enables querying data across multiple data sources like a single table. This feature simplifies data access and analysis by providing a unified view of your data.
- Understand Databricks Partner Connect: Partner Connect simplifies the integration of third-party data sources and services, allowing you to leverage external data assets within your Databricks environment.
- Load Data into the Azure Databricks Lakehouse: Look into the practical aspects of loading data into the Databricks Lakehouse and discover the ease and flexibility this powerful platform offers.
- Utilize Delta Live Tables: Delta Live Tables is a tool for managing data pipelines and workflows on Databricks. It lets you simplify data orchestration and automate ETL processes.
The migration process opens new possibilities and streamlines your data operations. However, it’s crucial to understand the key differences between an enterprise warehouse and the Databricks Lakehouse to ensure a smooth transition.
How is Databricks Lakehouse Different from Warehouse?
The Databricks Lakehouse is built on a foundation of Apache Spark, Unity Catalog, and Delta Lake. While it offers many advantages, it also has some fundamental differences from traditional enterprise data warehouses:
- CID Guarantees on Azure Databricks: ACID (Atomicity, Consistency, Isolation, Durability) guarantees are provided at the table level in Databricks, ensuring data integrity. However, there are no database-level transactions, locks, or guarantees.
- Data Objects in the Databricks Lakehouse: Databricks uses a three-tier namespacing pattern – catalog/ schema/ table, which differs from traditional databases. In Databricks, “database” and “schema” are synonymous, reflecting Apache Spark’s legacy syntax.
- Single Source of Truth: A core benefit of Databricks Lakehouse is creating a single source of truth by enabling different teams to work with the same data in a collaborative environment. This eliminates data silos and fosters better data governance.
- Data Skipping with Z-Order Indexes for Delta Lake: Delta Lake’s Z-Order indexing significantly improves query performance by organizing data to make common queries more efficient. This is a crucial optimization technique that differs from traditional data warehousing.
- Optimization Recommendations on Azure Databricks: Databricks offers a range of optimization techniques and tools to fine-tune query performance, making it essential to understand and leverage these for efficient analytics.
- SQL Language Reference: SQL is a universal language for data analysis, but there may be variations in the SQL dialect supported by Azure Databricks compared to your previous data warehouse. You can familiarize yourself with Databricks’ SQL syntax to ensure you can transition and data consistency.
Why You Should Consider Moving from Enterprise Data Warehouses to a Data Lakehouse
Transitioning from a traditional data warehouse to a data lakehouse presents compelling reasons for organizations seeking efficiency, flexibility, and cost-effectiveness. Here are key considerations that make this shift a strategic choice:
- Diverse Data Sources and Formats: Organizations use various data sources and file formats. The data lakehouse approach accommodates this diversity easily by storing incoming data in its raw form. This means no need for immediate data structuring or conversion. Databricks can load data from diverse data sources for utilization. This in turn allows data engineers to focus their efforts on the transformation process. This raw data storage preserves the native schema, data types, and file formats. This makes it a more efficient solution for handling a large and varied data ecosystem.
- Enhanced Data Analytics and Machine Learning: If your goal is to use the full range of your data through advanced analytics and machine learning, the data lake house architecture provides an ideal environment. The lakehouse’s open-source nature offers data scientists greater freedom to utilize popular programming languages like Python, R, Scala, and more alongside traditional SQL. This flexibility allows data professionals to explore, analyze, and model data in ways that suit their particular needs.
- Reduced Data Duplication and Movement: Traditional data warehouses often include the duplication and movement of data, resulting in increased storage costs and complexity. At the same time, a data lakehouse minimizes data redundancy by allowing data to remain in its raw form, reducing the need to copy or move data.
This simplified approach allows data analytics and machine learning processes. Overall, it leads to cost savings and improved efficiency.
Migrate From Data Warehouse to Databricks Lakehouse
The transition to a data lakehouse isn’t merely a technological shift; it’s a strategic transformation that aligns with the modern data landscape’s demands. Lakehouse is a more accessible partner, and this partnership can easily be achieved by shifting data warehousing workloads to Databricks.
It offers the agility to handle diverse data, allowing data scientists to use versatile tools, and optimizes data operations by removing unnecessary data movement. The data lakehouse approach is compelling for organizations seeking a more efficient and cost-effective data management solution.