As the big data analytics market rapidly expands to include mainstream customers, which technologies are most in demand and promise the most growth potential? The answers can be found in TechRadar: Big Data, Q1 2016, a new Forrester Research report evaluating the maturity and trajectory of 22 technologies across the entire data life cycle. The winners all contribute to real-time, predictive, and integrated insights, what big data customers want now.
10 TOP BIG DATA TECHNOLOGIES
- Predictive Analytics: software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources to improve business performance or mitigate risk.
- NoSQL databases: key-value, document, and graph databases.
- Search and knowledge discovery: tools and technologies to support self-service extraction of information and new insights from large repositories of unstructured and structured data that resides in multiple sources such as file systems, databases, streams, APIs, and other platforms and applications.
- Stream analytics: software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple disparate live data sources and in any data format.
- In-memory data fabric: provides low-latency access and processing of large quantities of data by distributing data across the dynamic random access memory (DRAM), Flash, or SSD of a distributed computer system.
- Distributed file stores: a computer network where data is stored on more than one node, often in a replicated fashion, for redundancy and performance.
- Data virtualization: a technology that delivers information from various data sources, including big data sources such as Hadoop and distributed data stores in real-time and near-real time.
- Data integration: tools for data orchestration across solutions such as Amazon Elastic MapReduce (EMR), Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB.
- Data preparation: software that eases the burden of sourcing, shaping, cleansing, and sharing diverse and messy data sets to accelerate data’s usefulness for analytics.
- Data quality: products that conduct data cleansing and enrichment on large, high-velocity data sets, using parallel operations on distributed data stores and databases.
Forrester’s TechRadar methodology evaluates the potential success of each technology and all 10 above are projected to have “significant success.” In addition, each technology is placed in a specific maturity phase—from creation to decline—based on the level of development of its technology ecosystem. The first 8 technologies above are considered to be in the Growth stage and the last 2 in the Survival stage.
Forrester also estimates the time it will take the technology to get to the next stage and predictive analytics is the only one with a “>10 years” designation, expected to “deliver high business value in late Growth through Equilibrium phase for a long time.” Technologies #2 to #8 above are all expected to reach the next phase in 3 to 5 years and the last 2 technologies are expected to move from the Survival to the Growth phase in 1-3 years.
Finally, Forrester provides for each technology an assessment of its business value-add, adjusted for uncertainty. This is based not only on potential impact but also on feedback and evidence from implementations and market reputation. Says Forrester: “If the technology and its ecosystem are at an early stage of development, we have to assume that its potential for damage and disruption is higher than that of a better-known technology.” The first 2 technologies in the list above are rated as “high” business value-add, the next 2 as “medium,” and all the rest “low,” no doubt because of their emerging status and lack of maturity.