Alfresco repository Caches optimisation can have significant impact on the performance of your Alfresco deployment. This post provides an overview on how the repository caches are implemented by Alfresco. The Alfresco repository leverages and provides in-memory caches. Memory caching (often simply referred to as caching) is a technique in which computer applications temporarily store data in a computer's main memory (i.e., random access memory, or RAM) to enable fast retrievals of that data. The RAM that is used for the temporary storage is known as the cache.

The term "real time" has been infused throughout tech, from real-time stock picks to real-time pizza tracking. As everyday enterprises begin incorporating data tools and tactics used inside the biggest of big tech companies, a sector of data services providers has emerged to help them take advantage of the truly real-time analytics and machine learning approaches only giant companies with far larger database teams and resources could have afforded in the past. Companies like Hazelcast, Rockset, Tecton and others enable split-second analytics and machine learning for things like financial fraud prevention, dynamic pricing or product recommendations that respond to what you just clicked. These companies promise to leave plodding batch-data processing for old-school business intelligence analysis in the dust. But whether every enterprise needs, wants or is ready to operate at a clip as fast paced as a Citibank, Uber or Amazon remains to be seen. Updating data every few days, every night or even every hour or so for business analysis using a typical batch processing approach "is like playing Monday morning quarterback," said Venkat Venkataramani, CEO and co-founder of Rockset, a company that provides a database for building applications for real-time data, analytics and queries.

To address latency challenges organizations are adopting streaming processing engines, in-memory computing database, and soon 5G wireless services. The rate at which transactions are processed has always been of critical importance in the enterprise. However, as organizations look to digitize business processes latency is rapidly becoming a bigger hurdle to overcome. A survey of 351 IT decision makers across five vertical industries conducted by Hazelcast, a provider of an in-memory computing platform, finds well over half of organizations (58%) are now are measuring performance in milliseconds and microseconds (58%) versus seconds. To put that in some perspective, the report notes the average blink of an eye occurs in 300 milliseconds.

Palo Alto, Calif., April 16, 2019 – Hazelcast, the leading in-memory computing platform company, today announced the general availability of Hazelcast Jet, the only streaming engine with no external system dependencies. The result is the industry's fastest stream processing engine that dramatically simplifies implementation from the smallest to largest deployments. Whether deployed in constrained environments, such as IoT sensors, or cloud-scale applications, Hazelcast Jet ingests, categorizes and processes vast amounts of data with ultra-low latency to support continuous intelligence practices. "SigmaStream specializes in high-frequency data and works with some of the world's largest companies that operate in the most constrained environments. By integrating Hazelcast Jet's high-performance streaming engine with our Hummingbird visualization and processing platform, we process high-frequency data from dozens of channels and address inefficiencies in real-time," said Hari Koduru, CEO of SigmaStream.