in

Top 10 Hot Big Data technologies

When big data analytics markets rapidly expand to include key customers, which technologies have the highest demands and promise the most growth potential? The answer can be found in the Tech Radar: Big Data, Q1 2016, a new Forrester research report that values the maturity and trajectory of 22 technologies throughout the data lifecycle. All the winners contribute to real-time knowledge, anticipation, and integration, what large customer data wants now.

Here’s my comment about 10 of the hottest big data technologies based on Forrester Analytics.

Predictive analytics:

Software and/or hardware solutions that allow companies to discover, evaluate, optimize, and deploy predictive models by analyzing large data sources to improve business performance or mitigate risk.NoQuery database: Database Locking, values, documents, and graphs.Search and explore Knowledge: tools and technologies to support extracting self-service information and new insights from large and unstructured data repositories located in multiple sources such as file systems, databases, streams, APIS, and other platforms and applications.Flow analysis: The software is able to filter, synthesize, enrich, and analyze high data throughput from various live data sources and in any data format.In-memory data structures: Provides low-latency access and handles large amounts of data by distributing data on dynamic random access memory (DRAM), Flash, or distributed computer system SSDS.Distributed file storage: A network of computers where data is stored on multiple nodes, often in copied style, for backup and performance.Data virtualization: A technology that provides information from a variety of data sources, including large data sources such as Hadoop and distributed data storage in real-time and near real-time.

Data integration:

Tools for coordinating data across solutions like Amazon Elastic MapReduce (EMR), Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB.Data preparation: The software eases the burden of sourcing, shape, cleaning, and sharing of diverse and messy datasets to accelerate the use of data for analytics.Data quality: Products that carry out cleaning and enriching data on large, high-speed datasets, using parallel operations across data warehouses and distributed databases.

The Forrester method slices Tech

Radar to evaluate the potential success of each technology and all of the above 10 things are predicted to have considerable success. In addition, each technology is placed in a particular mature phase from creation to rejection based on the degree of development of its technological ecosystem. The first eight technologies are considered to be at the stage of growth and the last 2 technologies in the survival phase.

Forrester also estimates that technology time will lead to the next stage and predictive analytics is the only tool that specifies > 10 years, which is expected to deliver high business value during the period of late growth through equilibrium periods for a long time. Education All Technology # 2 to # 8 above is expected to reach the next stage after 3 to 5 years and 2 final technology is expected to shift from the stage of living to the growth period in 1-3 years.

In the end, Forrester gives each technology an assessment of its business value, which is adjusted for uncertainty. This is based not only on potential impact but also on feedback and evidence from market implementation and reputation. Forrester says: If technology and its ecosystem at the initial stage of development, we have to assume that the possibility of its damage and disruption is higher than the famous technology. The first two technologies in the list above are appreciated as adding high-end business value, 2 technology followed by average, and all the remaining low technology, no doubt because of the emerging condition and lack of maturity.

Why did I add to the list of the two hottest technologies still in the preparatory stages of data and the data quality of the Survival phase? In the same report, Forrester also supplied the following data from the Q4 2015 survey of the 63 major data providers:
What is the customer’s level of interest for each of the following possibilities? (% response on the network very high)

Preparation and discovery of data 52%
Data Integration 48%
Advanced Analysis 46%
Customer Analytics 46%
Data Security 38%
In-memory computing 37%


While Forrester predicted that some independent providers of data preparation would survive, they believe that this is an essential capability to gain data Democrat, or rather its analysis, allowing scientists to devote more time to modeling and discovering the deeper understanding of users to have fun with the Data cascade. Data quality includes data security from the table above, in addition to other features that guarantee decisions based on reliable and accurate data. Forrester and expectation that data quality will have considerable success in the coming years as companies formally process the data certification. The data certification effort attempts to ensure that the data meets the standard of quality expectations; Protection; and regulatory compliance to support business decision-making, business efficiency, and business processes.

Big Japanese data as a theme of the conversation has come to the main audience perhaps more than any other word of tech. That does not help to discuss this unassuming term, which is defined for the east as the planet of the Islamic nervous system (see my statement here) or as a Hadoop title for the technical audience. The Forrester report, which helps clarify the term, defines big data as the ecosystem of 22 technologies, each of which has specific benefits for the business and through them, consumers.

Big data, namely a property of it, large volume, recently launched a new general discussion thread, artificial intelligence. The availability of very large datasets is one of the reasons Deep Learning, an extra set of AI, has been in the spotlight, from identifying Internet cats to defeating the go champion. In turn, ONE can lead to the appearance of new tools for collecting and analyzing data.
Forrester said: Besides more computational data and power, we have now extended analytical techniques such as deep learning and semantic service to the context of making artificial intelligence an ideal tool for solving a wide range of business issues. As a result, Forrester is witnessing some of the new companies that offer tools and services that try to support applications and processes with machines that mimic a number of aspects of human intelligence.

Anticipation is difficult, especially about the future, but the bet (relatively) safe that races mimic elements of human wisdom, led by Google, Facebook, Baidu, Amazon, IBM and Microsoft, all with very deep pockets, will change what we want to say is big data on the network in the very near future.

What do you think?

Comments

Leave a Reply

Your email address will not be published. Required fields are marked *

Loading…

0
How IT supports data science activities

How IT supports data science activities