In-Memory Database

What is an in-memory database?

An in-memory database (IMDB) is a type of computer database that stores data in the computer’s main memory (i.e., random-access memory or RAM). This is in contrast to storage in disks or other permanent storage media. An IMDB solution enables in-memory databases to process vast amounts of data quickly and with very low latency. This makes them useful in applications where real-time performance is critical.

In-memory databases are alternatives to disk-based databases, in which data is stored in nonvolatile media such as hard disks or magnetic tapes. The primary advantage of IMDBs is their fast access times and high throughput. By storing data in RAM instead of in a slower disk storage medium, in-memory databases can provide better query performance than traditional disk-based systems.

How do they work?

In-memory databases use in-memory data structures (e.g., arrays, linked lists, hash tables) to store and process data. This helps them achieve faster response times and higher throughput. In addition to in-memory data structures, they employ several functions to optimize queries, such as:

• Caching: They store frequently used data in RAM to speed up access and minimize disk input and output (I/O).
• Indexing: They use specialized index structures to look up data in large datasets quickly.
• Compression: They can compress and decompress data in RAM to reduce disk space requirements.
• In-place updates: They update data in their in-memory data structure, eliminating the need for disk I/O.

In-memory data structures usually organize information in a hierarchy of levels. This means that each level stores increasingly larger amounts of data. This hierarchical structure allows the IMDBs to process queries more efficiently by quickly locating relevant data. They can do this without searching through the entire in-memory data structure.

What are the benefits?

In-memory databases offer several advantages over disk-based databases, including:

• Real-time performance: With in-memory data structures and low latency, IMDBs can process large amounts of data in real time. This makes them useful in applications where speed is critical.
• Faster query processing: They provide faster query processing than disk-based databases, providing a better real-time experience for users.
• Reduced dependency on disk I/O: They reduce organizations’ reliance on expensive and time-consuming disk I/O operations.

How are in-memory databases used in business intelligence?

For modern organizations that leverage business intelligence (BI) technologies, the ability to quickly access and analyze large amounts of data in real time is critical. With in-memory databases, companies can gain valuable insights from their data in a fraction of the time it takes disk-based systems.

However, one of the downsides of solutions that rely exclusively on in-memory engines is that data is compressed. This means that queries are not fully leveraging all the underlying data. In contrast, a direct query approach enables people to issue queries directly against a data source and receive results in real-time, allowing analysis to take place on all data.

Regardless, IMDBs also are critical to the success of modern decision intelligence (DI) platforms, which represent the next evolutionary stage of business intelligence.

How can Pyramid Analytics help?

Pyramid Analytics’ next-generation DI platform leverages in-memory databases so that users in any part of an organization can quickly access and analyze large amounts of data in real time. The Decision Intelligence Platform also provides direct querying capabilities, which allow it to query data directly at the source. Because these platforms provide nontechnical business users with direct access to insights from analytics on a self-service basis, IMDBs are essential to providing users with the insights they need to make better decisions faster.

Contact us today to learn more about how we can help your business use analytics to optimize decisions with data.