Staying Ahead of the Competition with HTAP

Staying Ahead of the Competition with HTAP

In today’s fast-paced business world, keeping up with technology is vital for staying ahead of the competition. Hybrid Transactional Analytical Processing (HTAP) is a powerful tool that can provide organizations with real-time insights into their operational data, giving them a significant edge over their competitors. By analyzing data faster, businesses can speed up the delivery of value to their clients. This guide will explore the benefits and drawbacks of HTAP technology and provide practical steps for companies to implement it successfully.

Transactional vs. Analytical

There are two primary and wildly different workloads: transactional and analytical. Examples of transactional workloads include processing payments, managing reservations, and handling customer and inventory data. These use cases require low latency, data consistency, high availability, and so forth. Transactional databases like MySQL, Postgres, and SQL Server are specifically designed to meet these requirements, as they are optimized for millisecond record retrieval and concurrent updates.

Transactional vs. Analytical

However, analytical workloads, such as user behavior analysis, trend prediction, and report generation, help businesses gain actionable insights to drive crucial company decisions and boost revenue, streamline operations, and cut expenses. Data warehouses such as Teradata, Redshift, and Snowflake are optimized to summarize vast amounts of data. They can handle sophisticated and complex queries that could take many hours to process.

Traditionally, businesses would maintain separate databases for operational and analytical workloads. This helped improve analytical performance and minimize the impact of analytical workloads on the operational database. To bridge the gap between the two, the industry adopted Extract-Transform-Load (ETL) process, which involves extracting data from the operational databases, transforming it, and loading it into the analytical system. However, this process can take hours to complete, and managing multiple systems can become complex, slow, and expensive.

HTAP has emerged as a novel solution for businesses seeking to streamline their data management processes, offering a unified system that seamlessly handles transactions and real-time data analysis. Real-time analytics enables businesses to support cross-selling, dynamic pricing, and automate advertising campaigns. With HTAP, companies can stay competitive in today’s data-driven market without worrying about the hassles and expenses of running separate databases and building an ETL process.

How HTAP works?

It is great to hear that Hybrid Transactional Analytical Processing can combine both transactional and analytical workloads in the same database. But how is this possible? The key lies in the way data is stored. Row stores and column stores provide distinct ways to organize and store data in databases. Row stores are advantageous for transactional workloads, as they store complete records together. This improves the performance of reading and writing to a single record.

Conversely, Column stores arrange data in columns, with each column containing a single attribute of all records. This format is efficient for analytical workloads involving aggregations, as column stores are optimized for reading a single column efficiently. However, they are not optimal for transactional workloads, as reading and modifying a single record requires accessing and updating multiple columns.

Row store vs. Column store

HTAP solutions store data in both row and columnar formats, enabling databases to access data in the most suitable format for each workload. One challenge with this approach is that the columnar format is not optimized for writing a single entry each time. This is because the columnar format does not store the entry continuously, as is common in a row-oriented format. However, batching writes is relatively efficient in columnar format. Therefore, to address this challenge, HTAP solutions store newly written data in a write-optimized row format that is periodically converted to a read-optimized columnar format. This conversion from row to columnar format occurs automatically, transparently, and in the background, allowing for seamless integration of both workloads in a single database system.

Diagram of HTAP architectures

By leveraging the strengths of both row and columnar formats, HTAP systems deliver efficient processing for transactional and analytical workloads, resulting in optimal performance across a wide range of data processing scenarios. Utilizing both formats simultaneously enable real-time operational analytics, allowing businesses to run transactional and analytical queries on the same underlying data store for faster insights and more informed decision-making.

HTAP solutions

Hybrid Transactional Analytical Processing (HTAP) refers to the ability of a database to simultaneously handle both transactional and analytical workloads simultaneously, providing real-time insights. When the term was first defined by Gartner in 2014 and Forrester later referred to the technology as “Translytical”. Since then, several solutions have emerged to support HTAP workloads.

One of the most prominent solutions in the market is Oracle Database, which introduced its In-Memory Column Store in 2014. This feature-rich solution allows businesses using Oracle Database to run analytical queries with impressive performance without migrating the data. Oracle also offers MySQL HeatWave, an accelerator for MySQL that allows integration with data lakes, further improving its HTAP capabilities.

SAP HANA is another long-standing database that focuses on fast analytical responses while supporting transactions. Additionally, PingCAP has extended its highly scalable key-value store, TiKV, to support analytical queries via its open-source TiDB solution. Microsoft has also added support for near real-time analytics to SQL Server and Azure Cosmos DB, while Google recently announced AlloyDB for Postgres, a cloud solution that claims to be 100x faster for analytical queries. Snowflake has also introduced Unistore, a cloud-based platform that offers HTAP capabilities.

What are the benefits of using HTAP?

  • Real-time analysis of data: An insurance company implemented HTAP to enable real-time risk analysis and significantly improved their analytical query performance from 50x to 200x. This is just one example of the true power of real-time data in a competitive business environment where quick decisions are crucial. HTAP facilitates real-time analytics, enabling businesses to make informed decisions faster.

  • Reduce complexity: Forrester Research estimates that by 2024, the number of enterprises adopting HTAP seeking a common data platform that is easier to manage will double. Many companies are looking for alternatives because managing multiple data sources, systems, and processes is a challenging task with a lot of pitfalls. By having all the data stored in a single database, businesses can easily access, analyze, and manage their data. When using HTAP, companies can ensure that they have a single source of truth for their data. Having a single copy of the data simplifies data management and reduces the risk of errors and inconsistencies.

  • Cost-effective: A company with a focus on software automation for marketing reduced more than 50% of the costs by avoiding moving data between multiple systems. Switching to an HTAP solution can help save on costs by combining transactional and analytical processing on the same database system. These costs include, among others, hardware, training, software licenses, and maintenance. It also eliminates the need to operate and transfer data between separate systems, which can be costly, time-consuming, and error-prone.

When should I avoid using HTAP?

  • Single workload: If your business only has one type of workload, either transactional or analytical, then using HTAP may not be necessary. HTAP is designed to handle both workloads efficiently in a single database. In this case, opting for an HTAP solution can increase operational costs and complexity without providing any benefits.

  • Small datasets: HTAP may not be the best option if your business deals with small datasets. HTAP is designed for large datasets and complex queries, so consider other solutions if your datasets are small. Adding support for analytics adds storage and computational overhead with little to no benefit.

  • Growing features: Although HTAP systems are competent, they are not the best in class, either for transactional or analytical. Therefore, these databases do not support yet some features that your business may require. Before switching to a hybrid solution, decide which features you are using and plan for those you will need in the future.

  • Challenging integration: HTAP systems are designed to provide a unified storage platform for an organization's data. However, some use cases may require specialized databases with unique capabilities that can be difficult to integrate. While some HTAP solutions may offer integration with existing data lakes and data warehouses, it can still be challenging to seamlessly integrate different data sources.

Steps for Successful HTAP Implementation

To successfully implement HTAP in your organization, consider the following steps:

  1. Assess your business needs and goals: Understanding your business needs and goals is critical before implementing HTAP. Pinpoint all analytical queries that do not perform well in the current transactional database. Identify use cases where real-time analytics can provide the most value to your business. Most companies started using HTAP as proof of concept to solve a particular issue and expanded later to other use cases when they saw the benefits.

  2. Choose the proper database: Research different database options and choose a system that meets your business needs. Consider factors like data freshness, scalability, features, and workload isolation. Recognize the compromises each architecture must make to handle both transactional and analytical processing. For example, distributed databases like TiDB are more scalable but sacrifice data freshness. On the other hand, Oracle Database In-Memory offers great data freshness but lower analytical scalability. You can start experimenting with free tiers of different solutions to determine the best fit for your business.

  3. Plan your system architecture: Design your architecture with HTAP in mind. Some databases allow you to combine your transactional and analytical systems into one system. If you use a MySQL database, you can transition to an HTAP solution with minor changes by using MySQL HeatWave, for example. Other databases do not support HTAP and may require moving your operational data to a new system. Consider the impact of the changes on the business to make the transition as smooth as possible.

  4. Implement and test your system: Once you have designed your architecture, it is time to implement it. The implementation may involve migrating data from your existing systems to the new solution or rewriting transactional or analytical queries. Ensure that the systems are adequately tested so that no functionality is lost. Benchmark the transactional and analytical queries to understand the impact of HTAP.

  5. Monitor and optimize your system: After implementation, monitor your system and optimize it for performance. Typical steps to improve query performance are evaluating the query plan, adjusting database settings, and optimizing queries. Consider monitoring system metrics to understand where the database is underperforming.

If you are interested in learning more about HTAP or would like to implement it for your business, please feel free to contact me. I will gladly help you better understand HTAP and find the best solution for your unique business needs.