When to Choose Delta Lake, ClickHouse, Druid, or Vertica

Choosing the right storage and analytics engine can make or break your project. Today, we’ll dive into four popular options: Delta Lake, ClickHouse, Druid, and Vertica. We’ll compare them across key factors like user base, cost, limitations, deployment options, SQL support, and data storage format.

Understanding Your Needs

Before diving in, consider your specific use case. Here are some key questions:

• Real-time vs. historical analytics? Do you need queries on constantly arriving data, or historical analysis of large datasets?
• Query complexity? Are your queries primarily aggregations, or do they involve complex joins and filtering?
• Budget constraints? Are you looking for an open-source solution or willing to pay for a commercial license?

The Contenders:

Delta Lake (Open Source):

• Who: You like Databricks solutions you might want to use it, companies embracing open data lakes
• Cost: Free (built on Apache Spark)
• Strengths: ACID transactions, schema enforcement, time travel capabilities (versioning) for data lineage. Integrates well with Spark ecosystem.
• Limitations: Not built for real-time analytics. Schema enforcement can add overhead.
• Deployment: Local cluster with Spark or cloud platforms (Databricks, AWS, Azure)
• SQL Support: ANSI SQL with some Delta Lake specific extensions and external libraries and tools.
• Storage: Uses Parquet, a columnar storage format with efficient compression and fast queries on specific columns. Leverages Apache Spark’s indexing capabilities.
• Github Stars: 18.3k (as of March 2024)

ClickHouse (Open Source):

• Who: Companies seeking a fast, scalable analytical database for historical data.
• Cost: Free
• Strengths: Extremely fast for analytical queries on large datasets, especially aggregations. Highly scalable horizontally.
• Limitations: Not ideal for real-time ingestion or complex joins. Limited support for updates and deletes (future roadmap may change this).
• Deployment: Local cluster or cloud platforms (AWS, Google Cloud)
• SQL Support: ClickHouse SQL dialect (similar to standard SQL but with some extensions)
• Storage: Uses columnar storage with custom compression algorithms for optimized performance. Supports various indexing options.
• Github Stars: 30.2k (as of March 2024)

Apache Druid (Open Source with Commercial Options):

• Who: Companies requiring real-time analytics on high-velocity data streams. Popular in finance, IoT, and operational analytics.
• Cost: Free (open source) or commercially supported versions (Imply)
• Strengths: Excellent for real-time ingest and querying of fast-changing data. Highly scalable and fault-tolerant.
• Limitations: Not designed for complex ad-hoc queries or historical analysis. Schema is less flexible than relational databases.
• Deployment: Local cluster or cloud platforms (AWS, Google Cloud) with additional configuration for high availability.
• SQL Support: Limited SQL support (Druid SQL) focused on aggregations and filtering on specific dimensions and metrics.
• Storage: Uses a segmented storage format with data divided into time-ordered segments. Leverages various indexing techniques for fast retrieval.
• Github Stars: 15.6k (as of March 2024)

Vertica (Commercial):

• Who: Companies requiring a high-performance data warehouse for complex historical analysis.
• Cost: Starts with a licensing fee
• Strengths: Excellent for complex SQL queries on large datasets. Highly scalable and optimized for analytical workloads. Vertica offers full ANSI SQL support, allowing you to use all the familiar SQL constructs you’re comfortable with for complex data manipulation.
• Limitations: Not designed for real-time analytics. Expensive compared to open-source options.
• Deployment: Locally on dedicated hardware or cloud platforms (AWS, Azure)
• SQL Support: Full ANSI SQL support
• Storage: Uses columnar storage with proprietary compression algorithms. Supports various indexing options.

Choosing Your Weapon:

If real-time analytics are crucial, Apache Druid could be the timeseries way to go. For historical analysis with complex queries, ClickHouse or Vertica are strong options (depending on budget and on SQL query language needs). Delta Lake shines in data lakes requiring schema enforcement and time travel for historical analysis, but it’s not built for real-time workloads. Evaluate your specific requirements to make an informed decision.