Time and complexity are two of the greatest barriers organizations face in realizing the full value of their data. Hybrid transactional/analytical processing (HTAP) is a data architecture that joins online transactional processing (OLTP) and online analytical processing (OLAP) workloads, allowing one system to support both processing sets. Read on as we take a deeper look into what HTAP does.
Why HTAP Was Developed
Before HTAP, if an organization needed to run both OLAP and OLTP workloads, the configuration would require two separate databases, with each one optimized for the data workloads it was expected to perform. OLTP databases were needed for transactional processing, designed for executing single-record operations at scale with accuracy and speed, while OLAP databases were needed for multi-dimensional analytical queries and processing massive amounts of complex data. Making data from OLTP systems available for analysis in OLAP systems required data to be transferred from one system to the other using complex and time-consuming extract, transform, load (ETL) pipelines.
Although some organizations use an operational data store (ODS) as a way to bridge these two systems, this solution can be costly and complex to implement and fails to fully address the lag time in making transactional data available for analytical queries. ODS is more of a stop-gap measure that introduces an additional system that needs to be managed. HTAP was initially developed to support both OLAP and OLTP without creating additional layers of complexity.
Benefits of HTAP
Unifying transactional and analytical data unlocks many new opportunities to extract value from data stores. Although running parallel OLTP and OLAP systems is still common practice for many organizations, the advantages of uniting these two systems has made HTAP an increasingly popular choice.
Unifies siloed transactional and analytical data
When OLTP and OLAP databases run separately, organizations must contend with two disparate systems. Transactional data held in an OLTP system isn’t immediately available for analytical processing, making it difficult to take advantage of time-sensitive opportunities. By the time that data is loaded into the OLAP system, it can be days or weeks old. Use cases such as predictive analytics often rely on access to real-time or near real-time data.
Eliminates the need for ETL
HTAP can minimize the need for creating ETL pipelines to copy data from OLTP to OLAP databases. Moving data can be costly and resource-intensive. Since transactional and analytical data is handled together within the HTAP system, ETL pipelines aren’t necessary to make this data available for analytics.
Instantly run analytical queries on fresh transactional data
With data available for analytical querying as soon as it’s created, an HTAP database allows organizations to capture value from transient opportunities that would be lost in the time needed to transfer data from an OLTP to OLAP database.
Simplified data architecture reduces operational costs
With only one system to manage, HTAP reduces operational complexity and costs. An HTAP database architecture streamlines data management, freeing IT and data professionals to focus on higher-level, value-added tasks.
Snowflake Unistore: Hybrid Tables Powered by the Data Cloud
Snowflake’s Unistore takes a modern approach to HTAP by delivering benefits that surpass existing HTAP offerings.
Unistore streamlines transactional app development, allowing developers to build enterprise transactional apps with the simplicity, performance, ease, and near-infinite scale the Data Cloud offers. With a single data set, businesses can power new types of development, act on transactional data immediately, build better customer experiences, and get new insights by integrating transactional and analytical data in a single data set.
Simplify your architecture with a modern approach to working with data with the Snowflake Data Cloud.
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