Real-time computing refers to computing systems that respond to changes in the environment with millisecond constraints on their responses. Real-time data streaming hasn’t been traditionally feasible because of limited memory and disk speeds capabilities.
Today, RAM prices are at the lowest they have ever been and the continued development of SSDs has led to unprecedented data processing speeds. But to provide such services at scale requires complex software, expensive hardware and a wealth of information and resources most businesses have access to.
Instead, companies have to rely on service providers and frameworks that are known to have a history with near 100% uptime, are easy to integrate into monolithic systems and are affordable. Most programs are either too complex, lack distinguishing features or do not have enough commercial or community backing.
The following are some software and vendors that fit the bill.
1. GigaSpaces
GigaSpaces offers in-memory computing via an industry-leading in-memory data grid embedded within its InsightEdge platform. In doing so, it supports low-latency applications to deliver functionality like pub/sub messaging and data caching. Other essential functionality includes map/reduce, transaction management, data aggregation and event processing.
The InsightEdge is used to power real-time applications that require streaming data made more valuable by using a combination of machine learning and historical data. This data is used to enhance business operations in companies like Thompson’s Reuters and Barclays.
It is a combination of different software that are necessary for writing data driven applications. Some important ones include Spark, SQL and embedded machine learning software like Tensorflow. It can also access data from virtually any source—structured or unstructured. Hadoop, S3, Google Cloud and Azure are all supported.
GigaSpaces also offers enterprise solutions in the form of AnalyticsXtreme and MemoryXtend. The AnalyticsXtreme module delivers interactive queries and machine running models that can be run on streaming data and supports batch processing via Hadoop data lakes, Azure Blob Storage or Amazon S3 without a separate load procedure data duplication.
Combined with MemoryXtend, this enterprise solution lets businesses leverage features such as automatically move data for cold storage, archiving data to data lakes and warehouses and naturally order data in RAM or persistent memory. It efficiently stores data in the right layer based on performance without compromising infrastructure costs, enabling faster access and better, more actionable analytics.
2. memSQL
MemSQL is a relational database that relies on standard SQL drivers and queries to support queries for analytics and transactions. It is a popular solution among developers because of its data ingestion technology that allows developers to establish millions of connections and push many more events per day while allowing querying of data in realtime.
It leverages both in-memory and on-disk storage to make up for the volatile nature of RAM storage. This is an essential component in ensuring no data is ever lost. Significant speeds in processing will also be gained thanks to its in-built support for parallelism by distributing the load to different servers.
Companies like Pinterest use memSQL in conjunction with other real-time processing software such as Spark and Kafka to enable high-deliverability of resources and offer real-time analytics.
Lastly, its reliance on SQL makes it easy for developers to adopt into their current projects or otherwise learn. Any developer that has dealt with relational databases like MySQL, PostgreSQL and Oracle will have an easy time getting used to memSQL’s APIs since thanks to its ANSI SQL standard compliance.
3. Aerospike
Aerospike strives to be a real-time database that combines transactional and streaming data into a single system of parts that can process data just as fast as it comes in. It also supports the use of different frameworks, including machine learning by combining streaming data and historical data for faster insights into massive amounts of data.
The software has a different approach towards enabling sub-second latency as compared to memSQL and GigaSpaces’ SQL-like take on data processing. It encourages using fewer nodes and scaling them up rather than adding more nodes to across the system. This enables businesses to use fewer nodes with higher throughput per node, fewer chances of a node failing and lower costs of ownership. Ownership costs are a major concern for businesses that host their content on-premises.
Lastly, developers will appreciate Aerospike’s take on memory management. Allocation is handled natively rather than depend on a runtime system or a programming language. By extension, it manages to efficiently leverage system resources by storing the index inside RAM.
Conclusion
Applications of real-time data are all around us—from those we can’t see but interact with every day such as fraud prevention and e-commerce to more specialized use cases such as self-driving cars.
Since its importance is unlikely to dwindle any time soon, developers need to adopt the fastest, most efficient, feature-packed and user-friendly solutions they can find. As far as that goes, GigaSpaces, Aerospike, and memSQL are the running contenders.
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