Lakehouse Architecture

 Lakehouse Architecture vs. 2-Tier Data Warehouse Architecture:

1. Lakehouse Architecture

The Lakehouse Architecture is a new paradigm that combines the best aspects of both Data Lakes and Data Warehouses, offering the scalability and flexibility of a data lake while providing the performance and structure of a data warehouse.

• Benefits of Lakehouse Architecture:

  • Inexpensive Storage: Lakehouse uses a data lake for storing raw data, which is significantly cheaper compared to traditional data warehouses.
  • Scalability: It can scale to store vast amounts of data (even petabytes), which is ideal for big data applications.
  • Support for All Types of Data: It can store structured (relational data), semi-structured (JSON, XML), and unstructured data (images, videos, log files) in its raw/native form.
  • Open File Formats: Data in Lakehouses is stored in open file formats like Parquet, which is vendor-independent and allows seamless integration with various analytics tools.

• Challenges of Data Lakes:

  • No ACID Guarantees: Data Lakes generally do not provide ACID (Atomicity, Consistency, Isolation, Durability) guarantees, which are necessary for ensuring data integrity during updates and deletes.
  • Not Optimized for Reporting/BI: Data Lakes aren’t optimized for analytical workloads such as Reporting and Business Intelligence (BI). They are better suited for storing data, not for querying it.

2. Traditional 2-Tier Data Warehouse Architecture

Traditionally, companies used a 2-tier architecture that combines Data Lakes and Data Warehouses to handle different data workloads:

• Data Lake: The raw data is stored in the Data Lake.
• Data Warehouse: A structured, cleaned, and transformed version of the data is sent to the Data Warehouse to support Reporting and BI use cases.

Challenges of the 2-Tier Architecture:

• Requires Two Different Systems: Maintaining both a data lake and a data warehouse can increase complexity and overhead.
• Data Duplication Issues: Data is duplicated between the two systems (Lake and Warehouse), which can lead to inefficiencies and extra costs.
• Increased Costs: Running and managing two systems (Data Lake and Data Warehouse) increases operational costs.
• Additional ETL Activity: Data needs to be extracted from the lake, transformed, and loaded into the warehouse, leading to additional ETL processing.
• Stale Data: The data in the warehouse may not be up-to-date with the latest data in the lake due to the time lag in ETL processes.

Lakehouse Architecture Solves the Above Issues:

• Unified System: Lakehouse combines both raw and processed data in one system, eliminating the need for separate data lakes and warehouses.
• Reduced Data Duplication: Since raw and structured data can be stored in the same place, there is no need for duplication across systems.
• Eliminates ETL: Since both raw and structured data are housed together, ETL processes can be minimized or eliminated, reducing complexity and improving data freshness.

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Optimization Techniques in Databricks Lakehouse Architecture:

Databricks is a platform built to implement the Lakehouse architecture. It provides various techniques to optimize performance when dealing with large-scale data processing.

1. Data Skipping Using Stats

• Data Skipping allows for skipping irrelevant data during queries based on metadata stored for each file.
  • Each file in a Delta table has metadata that includes the min and max values for each column.
  • For example, if you query a column for a value between 100 and 200, the system doesn’t need to search through all the files. It can directly look at the metadata to determine which files fall within that range, skipping others.
  • This reduces the amount of data scanned, leading to performance improvements.

2. Delta Cache

• The Delta Cache stores data on the worker nodes' local disks for faster retrieval.
  • This is especially useful when performing repeated queries that involve the same dataset.
  • Enabling Delta Cache: You can enable the cache by setting the following in your Databricks cluster:

    spark.conf.set("spark.databricks.io.cache.enabled", "true")
    

  • Manual Cache: You can also cache specific datasets using the CACHE command:

    CACHE SELECT * FROM <Database-Name.Table-Name>
    

3. The Small File Problem

• Small files are a common issue in distributed systems, especially when you have many small files (e.g., 10 records per file).
  • In cases like this, the overhead of opening and managing multiple small files can be high, affecting query performance.
  • Example:
    • Table-A: 10,000 small files with 10 records each.
    • Table-B: 4 larger files with 25,000 records each.
    • Table-A will have more overhead when querying because many files must be opened and read, whereas Table-B is more efficient.
• Solution: Compaction or Bin-packing: This technique merges small files into larger files, reducing overhead and improving query performance.
  • Databricks provides the OPTIMIZE command to compact small files into larger ones:

    %sql
    OPTIMIZE <Database-name.Table-name>
    

4. Z-Ordering

• Z-Ordering is used to optimize data layout in the files, which helps achieve data skipping.
  • It is similar to creating clustered indexes in traditional databases.
  • Z-ordering sorts data within files based on a given column, improving the efficiency of filter, join, and group by operations by limiting the number of files that need to be read during queries.
  • Example of Z-ordering:

    %sql
    OPTIMIZE <Database-name.Table-name> ZORDER BY <Column-name>
    

5. VACUUM Command

• VACUUM is used to remove outdated files from the transaction logs, freeing up space and ensuring storage efficiency.
  • It removes files that are no longer needed (files that are not referenced in the latest transaction logs).
  • The retention period is configurable (default is 7 days), and files older than this threshold are removed.
  • Example of the VACUUM command:

    %sql
    VACUUM <Database-name.Table-name> RETAIN 1 HOURS DRY RUN
    

6. Optimized Writes

• Auto-compaction of small files is enabled automatically to improve write efficiency.
  • It helps in auto-combining small files into larger ones before they are written to disk, reducing write overhead.
  • This is controlled by setting the property delta.autoOptimize.optimizeWrite = true.
  • Auto-compact is used to automatically compact small files into larger ones once they are written, controlled by:

    TBLPROPERTIES ( delta.autoOptimize.autoCompact = true )
    

7. Photon Query Engine

• Photon is a C++-based vectorized query engine designed to speed up query execution by taking advantage of hardware optimizations.
  • It’s especially useful for compute-intensive queries like aggregations or complex transformations.
  • The Photon engine is enabled by setting the "Use Photon Acceleration" option when creating a cluster in Databricks.

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Summary

The Lakehouse Architecture addresses the limitations of the 2-tier data architecture by unifying the capabilities of both data lakes and data warehouses into a single system. It enables cheaper storage, faster data processing, and supports various use cases like Data Science, Machine Learning, BI, and Reporting.

The optimization techniques provided by Databricks—such as Data Skipping, Delta Cache, Z-ordering, VACUUM, and Photon Query Engine—further enhance performance by optimizing the storage and query execution processes. These optimizations help manage large-scale datasets efficiently, improving overall system performance.