Data Lakehouse

The lakehouse data platform ensures that data analysts and data scientists can apply the full and most recent data set toward business intelligence, big data analytics and machine learning. And having one system to manage simplifies the enterprise data infrastructure and allows analysts and scientists to work more efficiently.

Here we present the key features of data lakehouses and the benefits they bring to your organization.

Historically, you’ve had two primary options for a big data repository: data lake or data warehouse. To support BI, data science and machine learning, it’s likely that you’ve had to maintain both of these structures simultaneously and link the systems together. This often leads to data duplication, security challenges and additional infrastructure expense. Data lakehouses can overcome these issues.

A lakehouse typically consists of 5 layers: the ingestion layer, storage layer, metadata layer, API layer, and consumption layer. There are four main approaches to developing this architectural pattern. Your choice depends on your specific use case, requirements, and existing ecosystem. Evaluate the trade-offs between features, ease of use, and performance between these approaches:

1. Delta Lake: This open-source storage layer is the most popular approach today. It leverages ACID (Atomicity, Consistency, Isolation, Durability) compliance from transactional databases to enhance reliability, performance, and flexibility in data lakes. Delta Lake is particularly useful for scenarios requiring transactional capabilities and schema enforcement within your data lake. Notably, it offers features such as scalable metadata handling, data versioning, and schema enforcement for large-scale datasets, ensuring data quality and reliability for advanced analytics and data science tasks.
   
   As you can see, a Delta Lake uses APIs to integrate with your existing storage systems such as AWS S3 and Azure, compute engines including Spark and Hive, and APIs for Scala, Java, Rust, Ruby, and Python to make streaming and batch data available for analytics and machine learning.

2. Parquet and Apache Spark: You can use Apache Parquet as the storage format for your data lakehouse. Parquet is columnar storage optimized for analytics workloads. Combine it with Apache Spark for data processing, transformations, and querying. Although it lacks some features of Delta Lake (such as ACID transactions), it’s a lightweight option for certain use cases.

3. Hudi (Apache Hudi): Apache Hudi (Hadoop Upserts Deletes and Incrementals) is another open-source project that provides features similar to Delta Lake. It offers data versioning, schema evolution, and efficient upserts. You can use Hudi as an alternative to Delta Lake for managing data in your lakehouse.

4. Custom Solutions: If you have specific requirements, you can build custom solutions using a combination of storage formats (like Parquet or ORC), metadata management tools, and orchestration frameworks. However, this approach requires more development effort and maintenance.