Databricks: Best Practices
Performance Optimization, Unity Catalog, Data Lakehouse & Delta Lake.
What is Databricks?
Databricks is a cloud-based data platform that facilitates collaboration among data teams by unifying various data systems. It operates seamlessly on major cloud providers like AWS, Azure, and Google Cloud, allowing organizations to leverage their existing cloud infrastructure.
Data Lakehouse
Many companies face challenges with complex data architectures involving disparate data lakes and warehouses, often requiring multiple tools for analytics and data science. Databricks simplifies this landscape by offering a single platform that integrates all necessary functionalities, eliminating the need for multiple tools.
This platform is often referred to as a “data lakehouse,” which combines the capabilities of data lakes and warehouses into a single architecture. This approach enhances performance and optimizes costs by providing a cohesive environment for all data-related tasks.
Data warehouses vs. data lakes vs. data lakehouses
Data warehouses, data lakes, and data lakehouses are distinct data storage and management solutions:
- Data Warehouse: Stores structured, processed data for specific business purposes. It uses a predefined schema (schema-on-write) and is optimized for SQL queries and business intelligence.
- Data Lake: Holds raw, unprocessed data of all types (structured, semi-structured, and unstructured) in its native format. It uses a flexible schema-on-read approach, making it suitable for big data analytics and machine learning.
- Data Lakehouse: Combines features of both data warehouses and data lakes. It stores all data types like a data lake but adds a storage layer (e.g., Delta Lake) to provide data warehouse capabilities such as ACID transactions, data versioning, and schema enforcement.
What is Unity Catalog?
Unity catalog is a unified solution in Databricks to help implement data governance in Lakehouse.
Data governance is the process of managing the availability, usability, integrity, and security of the data in enterprise systems, based on internal standards and policies that also control data usage. The key benefit of data governance is that it ensures that data is consistent and trustworthy and doesn’t get misused.
Unity Catalog provides centralized access control, auditing, lineage, and data discovery capabilities across Azure Databricks workspaces.
Optimizing Performance at Databricks
The following optimization techniques can significantly improve query performance, resource allocation, and overall efficiency in Databricks environments which include:
Use Adaptive Query Execution (AQE)
This feature dynamically adjusts query execution plans at runtime based on actual data characteristics, improving performance for complex queries like joins.
Utilize Delta Cache
This feature accelerates data access by storing copies of remote data files on local storage, reducing latency for subsequent reads
Leverage Autoscaling
This feature automatically adjusts cluster capacity to handle varying workloads, ensuring efficient resource utilization.
Use Compaction
Running the OPTIMIZE command coalesces small files into larger ones, improving read query speed.
Implement Partitioning
Properly partitioning data can speed up queries by allowing Spark to skip unnecessary data partitions. e.g. partitioning, repartition (with shuffle), coalesce(without data shuffle).
Partitioning in Databricks
Partitioning in Databricks involves three main operations: repartition, partition, and coalesce. Here’s an overview of each:
Repartition:
- Redistributes data across a specified number of partitions.
- Useful for increasing or decreasing the number of partitions.
- Involves a full shuffle of data, which can be expensive for large datasets.
- Example:
df.repartition(100)
Partition:
- Used when writing data to create logical divisions based on column values.
- Improves query performance by enabling partition pruning.
- Commonly used with time-based columns or low-cardinality fields.
- Example:
df.write.partitionBy("year", "month").save("/path/to/data")
Coalesce:
- Reduces the number of partitions without a full data shuffle.
- More efficient than repartition when decreasing partitions.
- Does not guarantee balanced partitions.
- Example:
df.coalesce(10)
DBFS (Databricks File System)
DBFS is a distributed file system integrated into Databricks workspaces, designed specifically for efficient file management in big data processing and analytics environments. As a file management system, DBFS offers several key features:
- Unified Interface: DBFS provides a single interface to access files stored in various cloud object storage systems like Amazon S3, Azure Blob Storage, and Google Cloud Storage.
- Scalability: It can auto-scale to handle increasing data volumes without storage bottlenecks, leveraging the scalability of underlying cloud storage.
- Performance Optimization: DBFS is optimized for Spark workloads, offering high performance for reads and writes from Spark jobs, notebooks, and other analytics processes.
- File Format Support: It supports a wide range of file formats, including Parquet, Avro, JSON, ORC, CSV, and binary formats like images and audio files.
Delta Lake
Delta Lake, developed by Databricks, is an open-source storage layer that enhances data lakes with ACID (Atomicity, Consistency, Isolation, Durability) transactions, making it particularly suitable for master data management.
- Optimized Performance: Delta Lake is built natively on Apache Spark, offering 10–100x faster queries compared to traditional data lakes.
- Time Travel and Versioning: Delta Lake allows access to previous versions of data, enabling audits, rollbacks, and reproducibility of analyses
- Schema Enforcement: Delta Lake provides automatic schema validation, ensuring that only data conforming to the defined schema is written to the table
This was an overview of Databricks building blocks and most important features and at this point, you also learned about performance optimization techniques.
Thank you for reading!
