Alex Merced || 2024-10-21

Data Lakehouse || data lakehouse - data engineering - apache parquet

As data volumes grow and the need for scalable analytics increases, data lakes have emerged as a critical solution for organizations looking to store large datasets in their raw format. At the heart of these data lakes, Parquet has become a go-to file format due to its efficiency, flexibility, and ability to scale with modern big data systems. In this post, we’ll explore the role of Parquet in data lake architectures, how it powers modern data lakehouses, and why it is so well-suited for cloud-based, distributed environments.

What is a Data Lake?

A data lake is a centralized repository that allows organizations to store structured, semi-structured, and unstructured data at any scale. Unlike traditional databases or data warehouses, data lakes don’t enforce a strict schema on incoming data. Instead, data is ingested in its raw form, allowing for flexibility in how the data is stored and processed.

The key benefits of data lakes include:

  • Scalability: Data lakes can scale to petabytes of data, making them ideal for organizations with large datasets.
  • Cost Efficiency: Storing data in its raw form in cheaper storage (such as cloud object stores) is more cost-effective than using expensive relational databases or data warehouses.
  • Flexibility: Data lakes can handle a wide variety of data types, from raw logs and JSON files to structured CSV and Parquet files.

Parquet plays a crucial role in optimizing the performance of these data lakes by providing a highly efficient, columnar file format that improves both storage and query performance.

Why Parquet is Ideal for Data Lakes

The reasons Parquet is a preferred file format for data lakes boil down to several key features:

1. Columnar Storage

Parquet’s columnar storage model allows it to store data by column rather than by row, which is particularly useful in analytics workloads. In most analytical queries, only a subset of columns is needed. Parquet’s columnar format means that only the relevant columns are read, reducing I/O and speeding up queries.

For example, if your dataset contains 100 columns but you only need to run a query on 5 columns, Parquet allows you to access just those 5 columns, making queries more efficient.

2. Efficient Compression

Parquet supports multiple compression algorithms (e.g., Snappy, Gzip, Brotli), allowing it to reduce the size of large datasets stored in data lakes. Given that data lakes often store petabytes of data, reducing storage costs is a top priority. Parquet’s efficient compression helps organizations minimize storage usage without sacrificing performance.

3. Schema Evolution

As datasets in data lakes evolve, the ability to handle schema evolution is critical. Parquet supports schema evolution, allowing new fields to be added or existing fields to be removed without requiring a complete rewrite of the data. This flexibility is essential for maintaining backward and forward compatibility as data structures change over time.

4. Distributed Processing Compatibility

Data lakes are often built on top of distributed processing frameworks like Apache Spark, Presto, Dremio, and Apache Flink. Parquet is natively supported by these systems, enabling efficient processing of data stored in Parquet files. Its columnar format works well with distributed systems, allowing parallel processing of different columns and row groups across multiple nodes.

5. Partitioning and Predicate Pushdown

Parquet supports partitioning—a key feature in data lakes. Partitioning means that datasets are divided into smaller, more manageable chunks based on the values of certain columns (e.g., partitioning data by date). When queries are run on partitioned Parquet data, query engines can skip over entire partitions that do not match the query, drastically improving performance.

In addition to partitioning, Parquet’s predicate pushdown capability allows query engines to apply filters (predicates) directly at the file or row group level, avoiding the need to read unnecessary data. This is particularly useful in large-scale environments where minimizing data read is crucial to maintaining performance.

Parquet and the Data Lakehouse

In recent years, a new architecture has emerged that builds on the strengths of data lakes while addressing some of their limitations: the data lakehouse. A data lakehouse combines the flexibility of data lakes with the performance and data management features of traditional data warehouses. Parquet plays a central role in enabling data lakehouses by serving as the foundational file format.

How Parquet Fits Into Data Lakehouses

Data lakehouses leverage Parquet to provide the following benefits:

  • Transactional Capabilities: Data lakehouses often use transactional layers like Apache Iceberg, Delta Lake, or Apache Hudi to provide ACID (Atomicity, Consistency, Isolation, Durability) guarantees on top of the Parquet format. This allows for time-travel queries, versioning, and consistent reads, features that are crucial for enterprise-grade data management.

  • Efficient Query Performance: Lakehouses use Parquet as their default storage format due to its columnar design and compression capabilities. Combined with features like data reflections (in Dremio) and materialized views, Parquet files in a data lakehouse are optimized for high-performance queries.

  • Data Governance: Data lakehouses provide better data governance compared to traditional data lakes. Parquet, along with these additional transactional layers, allows for improved schema enforcement, auditing, and access controls, ensuring that data remains consistent and compliant with organizational policies.

Parquet with Apache Iceberg, Delta Lake, and Hudi

Apache Iceberg, Delta Lake, and Apache Hudi are all technologies that extend data lakes by adding ACID transactions, schema enforcement, and time-travel capabilities. Each of these technologies uses Parquet as a foundational file format for storing data:

  • Apache Iceberg: Iceberg provides table formats for managing Parquet files at scale, supporting large datasets with features like partitioning, versioned data, and fast scans.

  • Delta Lake: Delta Lake adds ACID transactions and time-travel features to data lakes, making it easier to manage large-scale Parquet datasets with consistent reads and writes.

  • Apache Hudi: Hudi provides transactional write operations and version management for Parquet data stored in data lakes, ensuring that data remains queryable while handling schema changes and streaming ingestion.

Best Practices for Using Parquet in Data Lakes

To get the most out of Parquet in data lake architectures, here are a few best practices:

1. Use Partitioning for Large Datasets

Partitioning is essential when working with large datasets in data lakes. By partitioning data based on frequently queried columns (e.g., date, region), you can minimize the amount of data read during queries and improve overall performance.

2. Leverage Compression

Compression is crucial for reducing storage costs in data lakes. Use compression algorithms like Snappy for fast compression and decompression, or Gzip/Brotli if you need to prioritize smaller file sizes. The choice of compression algorithm depends on the specific workload and storage requirements.

3. Enable Predicate Pushdown

Predicate pushdown ensures that only relevant data is read from Parquet files during queries. Ensure that your data processing frameworks (e.g., Spark, Presto, Dremio) are configured to take advantage of predicate pushdown to skip over irrelevant data and improve query speeds.

4. Combine with Transactional Layers

If you’re building a data lakehouse, consider using Apache Iceberg, Delta Lake, or Apache Hudi to add transactional capabilities on top of your Parquet data. This enables ACID compliance, versioning, and time-travel queries, which are crucial for enterprise-level data management.

Conclusion

Parquet has become the backbone of modern data lake architectures due to its efficiency, flexibility, and compatibility with distributed processing systems. Whether you’re building a traditional data lake or a more advanced data lakehouse, Parquet provides the foundation for scalable, high-performance data storage.

In the next post, we’ll explore performance tuning and best practices for optimizing Parquet to ensure that your data pipelines are running at their best.

Stay tuned for part 10: Performance Tuning and Best Practices with Parquet.