Collection: Big Data

Businesses switch big data platforms based on scalability, real-time processing capabilities, cloud integration, automation, and cost efficiency. Below is an analysis of which platforms companies replace and why.

1. Snowplow → Apache Spark / DataBricks

• Who switches? Companies needing real-time data processing and machine learning integration.
• Why?
  • Apache Spark offers in-memory data processing, improving speed for analytics workloads.
  • DataBricks provides a fully managed cloud-based Spark environment with AI and ML capabilities.
  • Snowplow is strong for event data collection but may not scale well for complex analytics workflows.

2. Informatica → Apache Hadoop / Cloudera

• Who switches? Enterprises moving to open-source or cloud-based big data frameworks.
• Why?
  • Apache Hadoop is open-source and cost-effective for handling large-scale batch data processing.
  • Cloudera provides enterprise-grade Hadoop solutions with enhanced security.
  • Informatica is a leading ETL tool but can be expensive for data-heavy enterprises.

3. Apache Hadoop → Amazon Elastic MapReduce (EMR) / Azure HDInsights

• Who switches? Businesses adopting cloud-native big data processing.
• Why?
  • Amazon EMR offers scalable Hadoop processing in AWS with pay-as-you-go pricing.
  • Azure HDInsights provides a managed Hadoop solution in Microsoft’s cloud ecosystem.
  • Apache Hadoop requires on-premises infrastructure, making cloud adoption more attractive.

4. Apache Spark → Snowplow / Apache Flink

• Who switches? Organizations prioritizing event-driven analytics or real-time processing.
• Why?
  • Snowplow is more optimized for event tracking and behavioral data analytics.
  • Apache Flink provides better real-time data processing compared to Spark's micro-batch architecture.
  • Apache Spark is powerful but may not be ideal for real-time stream processing at scale.

5. Teradata → Cloudera / Snowflake

• Who switches? Enterprises shifting to cloud-based big data warehousing.
• Why?
  • Cloudera provides hybrid cloud support with modern analytics capabilities.
  • Snowflake offers a fully managed cloud data warehouse with flexible compute pricing.
  • Teradata is a legacy data warehouse with high operational costs for on-premises deployment.

6. DataBricks → Snowflake / Apache Beam

• Who switches? Businesses looking for a more scalable data warehousing or ETL solution.
• Why?
  • Snowflake provides a better cost-to-performance ratio for structured and semi-structured data.
  • Apache Beam enables batch and stream data processing across multiple frameworks.
  • DataBricks is powerful for AI and ML but can be overkill for simpler data transformation needs.

7. Apache HBase → Amazon EMR / Cloudera Impala

• Who switches? Companies requiring better cloud-native or SQL-based big data solutions.
• Why?
  • Amazon EMR simplifies HBase deployments with managed services.
  • Cloudera Impala enables real-time SQL queries on big data.
  • Apache HBase is strong for NoSQL but lacks a robust SQL querying layer.

8. Cloudera → Snowflake / Azure HDInsights

• Who switches? Enterprises needing better cloud elasticity and multi-cloud support.
• Why?
  • Snowflake offers easier multi-cloud deployments and elastic compute pricing.
  • Azure HDInsights provides better Microsoft ecosystem integration for enterprises.
  • Cloudera is strong for on-premises big data but may not be as cloud-friendly as alternatives.

9. Apache Oozie → Apache Airflow / Prefect

• Who switches? Data teams needing better orchestration and workflow automation.
• Why?
  • Apache Airflow offers flexible DAG-based pipeline scheduling and orchestration.
  • Prefect provides a more modern, Python-based workflow management system.
  • Apache Oozie is Hadoop-specific and lacks modern cloud-native workflow automation features.

10. Hortonworks → Amazon EMR / Apache Beam

• Who switches? Companies migrating to cloud-based data pipelines.
• Why?
  • Amazon EMR provides a fully managed cloud Hadoop and Spark environment.
  • Apache Beam enables batch and stream processing across cloud and on-prem environments.
  • Hortonworks merged with Cloudera, but many businesses prefer native cloud solutions.

Summary of Big Data Solution Replacements

Why Do Businesses Switch Big Data Solutions?

✅ Cloud Migration & Scalability – Companies replace Apache Hadoop with Amazon EMR or Azure HDInsights for cloud-based processing.
✅ Real-Time Data Processing – Businesses switch from Apache Spark to Apache Flink for low-latency stream processing.
✅ Cost Reduction & Flexibility – Enterprises replace Teradata with Snowflake or Cloudera for more affordable cloud storage and analytics.
✅ Workflow Automation – Data teams move from Apache Oozie to Apache Airflow or Prefect for more efficient pipeline orchestration.