Columnar Database

What is a columnar database?

Columnar databases, also known as column-oriented databases, store data in columns instead of rows. Users can pull specific column information from the database as needed.

Columnar databases offer unique advantages in various scenarios. Many organizations rely on columnar databases software to process analytical questions faster and more efficiently.

Benefits of columnar databases

Columnar databases help developers scan through data. Below are some more significant advantages. 

• Performance: Columnar databases perform better for commonly used queries like grouping, sorting, and aggregating data points.
• Cost savings: Developers get better performance with comparatively less hardware, cutting down on storage costs.
• Improved productivity and insights: Columnar databases improve performance for analytical queries by orders of magnitude. It’s easier for developers and analysts to iterate and develop ideas about how to use the data. It means more productivity as queries find data in seconds. 
• Multipurpose: Along with big data applications, columnar databases also assist with online analytical processing (OLAP) cubes, storing metadata, and real-time analytics. They excel at multitasking as they can rapidly load new data without lags.
• Compressible data: Data can be highly compressed in columnar databases. It facilitates operations such as MIN, MAX, SUM, COUNT, and so forth.
• Self-indexing: Columnar databases use less disc space compared to traditional databases. As each index key’s columns have different indices, it reduces the amount of data stored in the disc.

Columnar databases storage formats

As data grows, so do processing and storage expenses. Columnar storage formats are two separate implementations defining how data is organized and housed. 

• Parquet is a popular columnar storage format, commonly used in big data processing frameworks. Examples are Apache, Hadoop, and Spark.

• Apache ORC, or optimized row columnar (ORC), is a high-performance columnar storage format for data processing frameworks. It provides efficient storage, compression, and execution of queries for analytical workloads.

Uses cases of columnar databases 

Columnar databases are best known for their high performance and efficient storage. Four prominent use cases take advantage of the columnar databases’ specific benefits.

• Data warehousing: Since columnar databases work efficiently on large data volumes, they’re a common choice in warehousing environments that store a lot of information from multiple sources. It provides storage through compression, utilization, and faster query response. It’s also responsible for managing the way huge datasets in cloud data warehouses are preserved. 

• Big data analytics platform: Column-based databases’ compression techniques and their ability to select targeted columns make it a relevant choice for big data analytics.

• Machine learning and artificial intelligence (AI) workloads: Both these use cases require complex data transformation and feature engineering. The columnar database's optimized retrieval and query performance speeds up these operations. This means faster model training and experimentation. Machine learning tools support storage formats, such as Parquet or ORC, to provide a consistent and efficient processing experience. 

• IoT data processing. Columnar databases are also popular in the Internet of Things (IoT). When IoT data comprises diverse attributes, such as sensor readings per device, columnar databases help reduce storage requirements. Moreover, they also support schema evolution, which is crucial in a dynamic IoT environment.

Best practices for using a columnar database

Columnar databases offer several benefits to their users. However, certain factors must be implemented to use columnar databases successfully. Here are some of the best practices users can follow.

• Understand data and workload: Users must know the data characteristics and specific analytical workloads well. Perform analysis on queries, patterns, and performance requirements to understand which columns to prioritize. 
• Select the correct format: Analyze various formats and features such as compression capabilities, schema evolution support, and ecosystem support.
• Optimize organization and compression: Test compression techniques to find the right balance between storage efficiency and query performance.
• Plan schema evolution: Plan for a data evolution in advance when there is a chance for the data schema to evolve. Consider the proper storage format for the schema evolution and design new strategies to handle schema changes without interfering with the existing processes. 
• Monitor performance: Keep a record of query execution time, data ingestion, and storage utilization to understand the areas to optimize. Review and fine-tune configurations regularly from evolving data and workload patterns. 

Columnar database vs. relational database

Database management systems use a columnar database to store data in columns. It reduces the time required to pull a query and improves the performance of input/output. 

Commonly used in data analytics and data warehousing, columnar databases play a significant role in reading and writing data. For example, a company records employees and departments in a series or one next to another. It helps to extract similar information as the data in the column are grouped.

Relational databases record data in long rows. It’s also known as a traditional database. For example, when a company records all the employees, it’s stored in rows.

Relational and columnar databases are used in data analytics and warehousing. However, the user decides which to employ based on their requirements. Both approaches are used in different scenarios.

Learn more about relational databases and understand why they’re popular.