Choosing the Right Approach: When to Use a Columnar Database, a Data Lake, or a Lakehouse Architecture

I.  In a data lake architecture, when to use a columnar Database i.e. RedShift or Snowflake

1. Analytical Workloads: Because of their architecture, which is optimized for high-performance searches on huge datasets, columnar databases are perfect for analytical workloads. When query performance, aggregations, and reporting are prioritized over transactional activities, these databases perform exceptionally well.

2. Fast Query Performance: For structured data analytics, databases such as Google BigQuery, Snowflake, and Amazon Redshift offer optimized query execution and indexing.

3. Cost-Effective Query Execution: Because columnar databases optimize storage, minimize compute resource utilization, and reduce input/output, their architecture naturally facilitates cost-effective query execution.

4. Aggregation-Heavy Queries: Business intelligence (BI) and data warehousing systems rely heavily on aggregation-heavy queries. Operations like SUM, AVG, COUNT, MIN/MAX, or GROUP BY are frequently used in these queries to summarize data for reporting, analytics, and decision-making.

5. Operational Dashboards/Data Warehouse: An OLAP-optimized database, such as Redshift, BigQuery, or Azure Synapse, is helpful if we require quick response times for BI tools or dashboards.

6. Indexing and Complex Queries: Databases provide sophisticated indexing and querying features (such as joins and filters) that pure data lake query engines may not be able to provide as quickly.

7. Data Consistency: Databases ensure consistency for crucial activities by assisting in the enforcement of ACID characteristics.

II. In a data lake architecture, when to not to use a columnar database

1. Large volumes of raw data from diverse sources

2. Unstructured/Semi-Structured Data: Most of your data (such as images, logs and videos) is either evaluated in bulk or accessed infrequently.

3. Big Data Analytics : Direct querying of the lake's raw data is possible without loading it into a database thanks to distributed query engines like Presto, Athena, or Trino.

4. Cost-effective storage for infrequently accessed data: Large datasets that may not be accessed often but may be useful for future analysis can be archived in data lakes because they are generally less expensive to store than traditional databases.

5. Cost Efficiency : Compared to querying directly from a data lake, databases can be costly for petabyte-scale storage and retrieval.

6. Low Query Performance Requirements: Directly accessing data from cloud storage formats like Parquet or ORC can be adequate if query time is not a concern.

7. Machine learning and data preparation: When the original data format may not be the best for analysis, data lakes can be utilized as a staging place to clean and prepare huge datasets before feeding them into machine learning models.

III. Modern and Hybrid Method: Data LakeHouse

1. Databricks: combines Delta Lake and Apache Spark to support transactions and index data.

2. Apache Iceberg/Apache Hudi/ Delta Lake: Gives our data lake transactional and indexing capabilities.