Hadoop vs Pachyderm: What are the differences?

1. Data Processing Approach: Hadoop uses a batch-processing approach for handling large volumes of data, where data is stored in HDFS (Hadoop Distributed File System) and processed using MapReduce. On the other hand, Pachyderm employs a data lineage approach, enabling data versioning and reproducibility by treating data as a series of immutable versions.

2. Scalability: Hadoop is known for its horizontal scalability by adding more nodes to a cluster to handle increasing data volumes and processing requirements. In contrast, Pachyderm provides a different scalability model based on containerization and Kubernetes, allowing users to scale data pipelines independently of underlying storage.

3. Data Versioning and Lineage: Pachyderm excels at data versioning and lineage tracking, maintaining a detailed history of changes made to data and enabling users to trace back to previous versions easily. In contrast, Hadoop does not inherently focus on data versioning and lineage management, which can be challenging in some use cases.

4. Processing Flexibility: Hadoop is primarily focused on batch processing workloads, while Pachyderm provides more flexibility by supporting batch, streaming, and machine learning workloads within the same platform. This versatility allows users to handle diverse data processing requirements efficiently.

5. Metadata Management: Hadoop requires additional tools or frameworks for metadata management, such as Apache Hive or Apache HBase, to handle metadata associated with data processing. In contrast, Pachyderm integrates metadata management within its platform, simplifying the process of organizing and querying metadata related to data operations.

6. Concurrency Handling: Pachyderm offers better support for concurrency by enabling multiple users to work collaboratively on different data pipelines without conflicts, thanks to its containerized approach and versioning capabilities. In comparison, Hadoop may face challenges with concurrent data processing tasks that require careful coordination to avoid data inconsistencies.

In Summary, Hadoop relies on batch processing with HDFS and MapReduce, while Pachyderm emphasizes data versioning, scalability with containers, and processing flexibility.