Row store and Column store databases are two different approaches to retrieve and store data from Database Management Systems (DBMS). Both databases are appropriate for many use cases, and each has unique qualities.
Column store databases can handle huge chunks of data very efficiently, which helps the database to perform better analytical processing, compression, and schema flexibility. On the other hand, row store databases handle real-time transactional workloads. Row-oriented databases are used for transactional processing and strict schema.
Both databases have a significant use case for ETL (Extract, Transform, Load) practices. While performing ETL, we usually fetch data from row-based databases and then store the data in columnar databases. This allows you to perform data analysis at scale.
Let’s discuss column store vs row store database in detail and learn about five critical ways they differ.
Overview of Row Store Database
A row store database is a DBMS that stores and organizes the data in a row-oriented format. In this database, each table row is treated as a separate entity that includes data values and attributes related to that record. It is a more traditional way of storing the data.
Imagine a system for tracking customer orders at an online business. Each order is stored as one complete row in the database with all the details, such as customer name, gender, country, and more. This makes a row store database compatible with transactional systems, where records are inserted, retrieved, or updated frequently.
Therefore, row store databases are best suited for OLTP applications because of their matching characteristics and features, such as transactional processing, writing performance, etc.
Some well-known row-oriented databases include MySQL, Oracle, PostgreSQL, Microsoft SQL server, etc.
You can move your data from the row store database to the column store database using automated platforms like Hevo Data.
Hevo provides several unique features, including the Destination Workbench, which helps you verify the data type of the loaded data. It also extends flexibility by allowing you to create column-based partition tables on both ingestion time-based and time-unit (date or timestamp) from its user-friendly interface.
Ingestion time-based partition enables you to create a column where you can store the time of ingestion of every row. It is frequently leveraged for reporting and auditing. On the other hand, time-unit partitions help you to create daily, monthly, or yearly partitions from the date column. It is used for grouping data on specific time intervals for obtaining in-depth insights.
Key Features of Row Store Database
Below are some of the important features of the row store database:
- Transactional Processing: These databases are designed to handle transactional workloads where data consistency, real-time processing, and integrity are essential. As a result, row store databases are suitable for use cases in almost all kinds of organizations.
- Strict Schema: Row store databases have a strict schema design. It requires a predefined schema with fixed data types and columns to guarantee data integrity and consistency.
- Write Performance: Row store database excels in writing performances. It makes writing and updating operations easier because it stores related data in a row. Therefore, you can perform insertion, updation, and deletion operations without constantly traversing through different rows to find related data points.
- Real-time Data: A row store database is a good choice when you need to access and process the data as it is generated. Its fixed schema behavior prevents the database from performing many operations to filter related data types. This feature makes the database ideal for applications like e-commerce, where real-time transactional processing is required.
Overview of Column Store Database
A column store database is a DBMS that organizes and stores the data in a column-oriented format. Unlike the traditional row-based database, where a row contains different columns of a single record. A columnar database stores the data of other rows together in a single column.
For example, consider a library that maintains records of all its books; the information includes title, author, genre, and price. In the row store database, each value of title, author, genre, and price would be stored together in a block. It is a technique to divide the data into equal sizes and store the records in data storage systems.
In other words, each block would store the records of each row in a sequence. However, if the record size exceeds the block size, each row data could be stored in multiple blocks. So, to fetch all the values of a single column—price—to find the average price of books, you need to traverse through all the blocks (every row) to retrieve the desired data.
With this technique, you are also moving through data points of other columns that you do not need, making it a slow-paced process. However, in the column store database, the records of title, author, genre, and price are stored separately. Case in point, a block would contain all the titles of all the books one after the other, and another block would contain all the records of price, and so on.
So whenever you want to access all records of titles, authors, genres, or prices, you can fetch the data quickly since you are not traversing through data of other columns. This makes column-store databases very efficient in retrieving and storing, particularly for data analytics workloads.
Therefore, column store databases are best suited for Online Analytical Processing (OLAP) applications.
Some well-known column store databases in this DBMS are Apache Cassandra, Amazon Redshift, MariaDB, Snowflake, etc. Click here to learn about columnar store databases in detail.
You can move your data from the row store database to the column store database by using SaaS tools like Hevo. It is designed to streamline your database integration process end-to-end. Hevo takes care of most of the technical work for you, including data deduplication and historical data syncing, all while working in the background.
Key Features of Column Store Database
The column store database has many features. Some of the important ones are as follows:
- Compression: The column-store database employs specialized compression algorithms like run-length encoding and dictionary encoding. It helps optimize the space in the database.
- Query Performance: Columnar database excels at efficient data retrieval and manipulation, especially with large datasets. The query performance becomes efficient as compared to a traditional database. Therefore, data scientists and other data professionals use columnar databases for business intelligence to obtain insights quickly.
- Schema Design: Column store databases are known for schema flexibility. The flexibility of the schema allows you to handle semi-structured data without much hassle. Today, most of the real-world data is either semi-structured or unstructured, thereby allowing you to run analysis on diverse data types.
Column Store vs Row Store: 5 Critical Differences
Here are five critical differences between row store and column store databases as follows:
The column store database is primarily used for analytical workloads. It provides unmatched performance for challenging data analysis tasks. The column-oriented storage allows for quick filtering, aggregation, and data manipulation. These capabilities position it as an ideal choice for carrying out in-depth analysis.
On the other hand, row store databases are focused on transactional workloads and fixed data integrity, making them ideal for transactional workloads. Although this database can handle analytical queries, the performance lags behind while handling huge datasets. Because of the row-oriented approach, this database is less efficient than columnar databases in analytical capabilities. Therefore, you should consider a row store database for real-time transactional systems.
In column store databases, data dependency is minimal, meaning each column operates independently of other columns. To decrease data dependency, columnar databases perform data encoding and compression techniques for specific data types. Because of its independence, it allows users to quickly retrieve data for carrying out data analysis.
Data dependency is more obvious in row-store databases, as this database is ideal where data relationships are essential, for example, for carrying out operations like identifying patterns or building recommendation systems.
Columnar store databases have exceptional data compression capabilities. Since the data stored in one column are of the same type, it provides exceptional compression ratios. This enables column store databases to reduce database costs by optimizing data storage space. Additionally, data compression helps with other advantages, like scalability and query performance.
On the other hand, row store databases use a row-level compression method. However, row-level compression is difficult due to the different data types in each block. You cannot apply compression techniques without considering the diversity of data types. This limits the type of compression you can achieve. Although row-level compression also saves storage space, columnar compression is more efficient.
Column store databases are known for schema flexibility. The schema flexibility of the database allows you to handle semi-structured data. With schema flexibility, you can perform data queries without rigid data modeling. Therefore, its flexibility is useful for performing analytical tasks with different data types, especially JSON and XML.
Conversely, row store databases have a more fixed schema approach. The database has a predefined schema structure and fixed data types for each record to store the data. Any change or adjustment in schema requires careful planning and database changes. While this feature is useful for data consistency, it limits you from carrying out analytics workloads with diverse data structures.
So, if you want to work with diverse data sources and perform analytical tasks, a column store database can give you that schema flexibility.
The pricing of both databases varies significantly.
The need for processing huge chunks of data and computational requirements makes column store databases costly. However, many features of this database, such as excellent query performance and effective storage, make the expenses on the database worth it. Higher costs can be justified for possible savings due to the efficiency of its operations.
On the other hand, row store databases are a more budget-friendly choice for simpler use cases. The affordability of this database aligns well with a wide range of applications where data consistency and real-time transaction handling are priorities. It is a better option for businesses that need databases for transactional services.
In conclusion, both databases have critical differences based on business use cases. As a result, most organizations are using a hybrid approach. It means that instead of choosing one database, organizations use both the Row store and Column store Database according to the requirements. In the hybrid approach, the row store database handles the real-time transaction recording, assuring data accuracy and consistency. And the column store database is used for analytical workloads due to its ability to compress data, support parallel processing, and more.
By balancing real-time transaction processing with high-performance analytics, organizations may carry out a variety of business requirements.
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