Best Practices for Using Cloud Storage with Apache Kafka for Efficient Long Term Data Management

Apache Kafka is widely known for its ability to handle real-time data streams with high throughput and low latency. But when it comes to managing long term data storage, Kafka’s native storage model has limitations. This post explores how well Apache Kafka manages long term data, the role of cloud storage buckets as an alternative, and best practices for combining Kafka with cloud storage for efficient data access and retrieval.

How Apache Kafka Handles Long Term Data Storage

Apache Kafka stores data in topics as immutable logs on local disks of Kafka brokers. This design supports fast writes and reads for streaming use cases. However, Kafka’s local storage is not optimized for long term retention of large volumes of data due to:

• Storage limits: Kafka brokers have finite disk space, making it costly and complex to keep data indefinitely.

• Retention policies: Kafka typically uses time-based or size-based retention to delete old data automatically.

• Recovery complexity: Restoring data from Kafka after broker failures can be challenging for very large datasets.

  
  

Kafka’s storage model is excellent for short to medium term data retention, often ranging from hours to weeks. For longer retention, organizations often look to external storage solutions.

Using Cloud Storage Buckets Instead of Kafka Queues

Cloud storage buckets such as Amazon S3, Google Cloud Storage, or Azure Blob Storage provide scalable, durable, and cost-effective options for long term data storage. Instead of relying solely on Kafka’s internal storage, many architectures offload older Kafka data to cloud buckets.

Effectiveness at Locating and Retrieving Messages

Cloud storage buckets are object stores, not message queues. This means:

• Data is stored as files or objects rather than individual messages.

• Retrieving specific messages requires indexing or partitioning strategies.

• Latency for access is higher compared to Kafka’s local storage.

  
  

To make retrieval efficient, data is often stored in formats and structures that support fast querying and partition pruning.

Data Formats and Partitioning: Parquet and ORC

Apache Kafka itself does not natively use Parquet or ORC formats. These columnar storage formats are popular in big data ecosystems for their compression and query efficiency.

When exporting Kafka data to cloud storage, many teams convert messages into Parquet or ORC files. This approach offers benefits:

• Efficient compression reduces storage costs.

• Columnar layout speeds up queries by reading only relevant columns.

• Partitioning by time, topic, or other keys enables fast filtering.

  
  

For example, a common pattern is to batch Kafka messages into hourly Parquet files partitioned by date and topic. This structure allows downstream analytics tools to quickly locate and scan relevant data.

Best Practices for Using Cloud Storage with Apache Kafka

1. Use Kafka Connect with Cloud Storage Sink Connectors

Kafka Connect provides ready-made connectors to export Kafka topics to cloud storage. These connectors handle batching, file format conversion, and partitioning automatically.

• Choose connectors that support Parquet or ORC output.

• Configure partitioning schemes aligned with your query patterns.

• Set appropriate flush intervals to balance latency and file size.

  
  

2. Implement Tiered Storage Architectures

Tiered storage separates hot data (recent, frequently accessed) stored in Kafka brokers from cold data (older, infrequently accessed) stored in cloud buckets.

• Keep recent data in Kafka for fast streaming and processing.

• Offload older data to cloud storage for cost-effective long term retention.

• Use tools like Apache Kafka’s Tiered Storage feature (available in some distributions) or custom pipelines.

  
  

3. Design Partitioning and Naming Conventions Carefully

Effective partitioning is key to efficient data retrieval in cloud storage.

• Partition data by date/time to enable time-based queries.

• Include topic or event type in partition keys for filtering.

• Use consistent file naming conventions to simplify indexing.

  
  

4. Use Metadata and Indexing for Fast Lookups

Since cloud storage is not a message queue, indexing metadata is essential.

• Maintain external indexes or catalogs (e.g., AWS Glue, Apache Hive Metastore).

• Use schema registries to track data formats and versions.

• Leverage query engines like Presto or Apache Spark that integrate with cloud storage and metadata.

  
  

5. Monitor and Manage Data Lifecycle

Set lifecycle policies on cloud buckets to manage data aging and cost.

• Archive or delete data after retention periods.

• Use storage classes (e.g., S3 Glacier) for infrequently accessed data.

• Automate cleanup to avoid unnecessary storage costs.

  
  

Real-World Example: Streaming Analytics Pipeline

A retail company streams transaction data through Apache Kafka. Recent transactions are processed in real-time for fraud detection. Older transactions are exported hourly to Amazon S3 in Parquet format, partitioned by date and store location.

Analysts query the S3 data using Amazon Athena, which reads Parquet files efficiently. This setup reduces Kafka broker storage needs and provides scalable, cost-effective long term storage with fast query performance.