5X vs Databricks: A Comparison on Core Data Readiness

Databricks, the powerful analytics platform built around Apache Spark, has emerged as a cornerstone in data engineering and analytics teams. Its lakehouse architecture, coupled with tools like Spark and SQL, has made it a popular choice for businesses handling vast datasets. Plus, the recent innovations in AI/BI, model quality, and AI governance are becoming crowd-pullers.

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But what about the core data readiness?

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The true measure of a data platform isn't query speed or storage capacity; it's data readiness. Clean, structured, and centrally modeled data is the fuel for BI, advanced analytics, data activation, and increasingly, AI. A solid data foundation is crucial for AI and LLMs to deliver accurate and valuable insights. You may have all the AI power, but without clean, accessible data, your models are just as good as their input. 

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The five layers of a data-ready system are:

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1. Ingestion

2. Warehouse

3. Modeling 

4. Orchestration

5. Business Intelligence

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How does Databricks measure up against these components of a data readiness platform? Let's find out.

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Databricks

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Ingestion

• Limited native data ingestion capabilities (Auto Loader, COPY INTO, Add Data UI).
• Requires additional configuration for complex ingestion pipelines.
• Relies on third-party tools and integrations for low-code, scalable data ingestion.

Warehouse

• Offers cloud data warehousing using lakehouse architecture and Databricks SQL.
• Databricks SQL supports open formats and ANSI SQL for queries and visualizations.
• Delta Lake provides ACID transactions for Spark workloads and schema evolution.
• Unity Catalog offers unified governance and data lineage.

Modeling

• Integrates with Delta Lake and Apache Spark.
• Offers Delta Live tables for building pipelines and ETL.
• Provides interactive notebooks to write Python code for wrangling, transformation, and model building.
• Uses Spark SQL for complex transformation and processing, and DataFrames for manipulation.
• Lacks an enterprise-grade modeling tool like dbt natively.

Orchestration

• Offers Databricks Workflows, a managed service for orchestrating data pipelines.
• Workflows can trigger notebooks, scripts, and jobs in a defined sequence.
• Integrates with Delta Lake for checkpointing and job re-runs in case of failures.
• Supports modular workflows but true nesting of DAGs within each other is unavailable currently.
• Doesn’t run a commercial-grade orchestrator (for highly intricate workflows with advanced dependency management and code reusability).

Business intelligence

• The newly launched Databricks AI/BI is built on a compound AI system to draw insights from data across Databricks.
• Dashboards provide a low-code experience for analysts, while Genie helps business users with self-serve analytics.
• Offers connectors for Tableau, Power BI, and Preset.

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How 5X complements Databricks’ warehousing capabilities

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Ingestion

• Offers 500+ pre-built connectors from all of the most used data sources.
• Hours, day implementations for custom connector development for the long tail of connectors.
• Simplifies handling incremental data updates for scenarios requiring near real-time data pipelines.
• Support for Apache Iceberg Tables in S3 or other flat storage.

Warehouse

• Works on top of Databricks.
• Also works with multiple other warehouses, including GBQ, Snowflake, and Redshift.

Modeling

• Integrates with dbt for enterprise-grade data modeling.
• Offers features like lineage tracking, version control, and modular transformations.
• Also supports SQL, Python, and notebooks for transformation flexibility.

Orchestration

• Offers Dagster to ship pipelines quickly with 1-click scheduling.
• Enterprise grade scheduling and DAGS with easy-to-use UI.
• Prebuilt templates to accelerate dev time.

Business intelligence

• Compatible with any BI tool.
• Provides Superset as an inbuilt option in the platform.
• Deep integrations and provisioning Power BI, Looker, Sigma and Tableau from 5X.

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Databricks vs 5X: A comparison on core data readiness

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Other considerations

Total cost of ownership (TCO)

• Databricks: Building a complete data pipeline on Databricks often requires additional tools like:

  • Data ingestion: Kafka, Debezium (licensing fees, infrastructure costs)
  • Data modeling: Notebooks, Spark SQL, Delta Live Tables (compute, storage costs)
  • Data warehouse: Databricks SQL
  • Orchestration: Airflow (infrastructure, maintenance, licensing)
  • Metadata management: Amundsen, Apache Atlas (open-source but operational costs)

• These tools add to the overall TCO due to infrastructure, licensing, and operational overhead.

• 5X: Consolidates these functionalities into a single platform. This eliminates the need for multiple tools and associated costs. This integrated approach can reduce TCO by 30-50% through simplified billing, reduced infrastructure, and operational efficiencies.

Integrated services offering

• Databricks: Needs huge resource allocation for building and managing data pipelines, including:

  • Data engineering team salaries
  • External consultancy fees
  • Infrastructure provisioning and management
• 5X: 5X’s integrated services are approximately 25% of the cost of US-based consultancies and 70% of the cost of building and scaling an in-house team in America.

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Summing up

Databricks is a powerful analytics platform that has rapidly gained prominence. Originating from the Spark ecosystem, it popularized the lakehouse architecture, combining the flexibility of data lakes with the structure of data warehouses. This approach allows you to store your data in any file format within flat storage and process it using tools like Spark, SQL, or notebooks. 

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Moreover, it’s making strides in innovation with SQL Serverless, MLflow advancements, and Databricks AI/BI showing commitment to improved performance, machine learning, and self-service analytics. 

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However, despite these launches, core data readiness aspects like ingestion and enterprise-grade modeling and orchestration remain areas with glaring gaps. To address these gaps and solidify your data readiness, use 5X on top of Databricks. With 5X integrated into Databricks, you can streamline your data prep and continue to take full advantage of Databricks' Spark, Workloads, AI, BI, and other capabilities.

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