Big data analytics is here to stay. To answer its needs, which big data technologies will become mainstream depends on their ability to be real- time, integrated, and predictive. Here are the top 10 big data technologies that have all these three qualities and more for your big data solution.
- Predictive analytics- This big data technology offers software and hardware that provides easy discovery, evaluation, and optimization for deployment of predictive models which can help improve business performance and mitigate business risk by analyzation of big data sources.
- Search analytics- This serves as a big data solution for extraction of knowledge from databases, file systems, APIs (Application Program Interface), and streams. Valuable information can be filtered and consolidated to speak insights about business from large repositories of structured and unstructured data.
- NoSQL (Not Only Structured Query Language) databases-Thisprovides for easy big data analytics of documents, graphs, wide column, and key- value. Its simplicity of design and data storage and retrieval of SQL and SQL- like languages makes it to this list.
- Distributed file stores-A computer network for data storage on many nodes together in a replicated manner for performance and redundancy. It helps to share information among different clients in a controlled way. The data can be accessed as if it is stored locally on a computer.
- Stream analytics-Tools in this big data technology can filter, enrich, aggregate, and analyze big data from multiple live sources and present the output in any format. Actionable information can be garnered through normalization, data manipulation, pattern- of- interest detection, advanced analytics, and cleansing.
- Data virtualization- This technology delivers insights from different big data sources including distributed data stores and Hadoop in real- time as well as near- real time. It has an advantage of fewer data errors and can be used for business intelligence, data services, enterprise search, cloud computing, master data management, and service- oriented architecture. So much for a big data solution!
- In- memory data fabric-Any distributed computer system can be distributed by data across its DRAM (Dynamic Random Access Memory), SSD (Solid State Drive), and Flash to process and low- latency access of humungous data.
- Data quality- Parallel operations can be run on databases and distributed data stores by this big data technology for data enrichment and cleansing of high- velocity data sets.Imagine how it can help in your business with decision making, operations, and planning!
- Data integration- A technique for data orchestration for various solutions such as Apache Pig, Apache Hive, Apache Spark, Couchbase, MapReduce, MongoDB, Amazon Elastic MapReduce (EMR), and Hadoop. Different computer systems, services, and middleware can be arranged, coordinated, and managed by automated methods.
- Data preparation-Software products for sourcing, profiling, validating, cleansing, shaping, and sharing diverse data for acceleration of usefulness of big data analytics for business analytics and business intelligence. Data consistency comes easy with this big data technology and can prevent the problem of data scientists investing much of their time in separating fit data from the unfit.
These big data technologies are on this list because of their market reputation, need by the business world, and ease of implementation in big data analytics. Want to make a change in 2017? Better hire talent who can provide big data solution in these dimensions.