Data science technology is important for better marketing. Many companies uses data to analyze their marketing strategies and create new advertisement.

1. Top 10 Data Science Technologies
Summary
As data science technologies are rapidly expanding to the market including mainstream customers, it is better to know which technologies are demanded
most and promise the most potential growth. This article points out 10 top big data analytics tools, being in extensive use today. However, this is based
on mainly Forrester Research report, 2016.
The top 10 big data technologies are- Predictive analytics, NoSQL databases, Search and knowledge discovery, Stream analytics, In-memory data fabric,
Distributed file stores, Data virtualization, Data integration, Data preparation (automation), and Data quality. The first eight technologies are considered
to be in the Growth stage while the last two in the Survival stage. Forrester for each technology provides an assessment of its business value-add,
adjusted for uncertainty. This is based on potential impact as well as on feedback and evidence from applications and market reputation.
With the rapid expansion of the big data analytics to the market including mainstream customers, it is better to know which technologies are demanded
most and promise the most potential growth.
Here is the list of top 10 big data technologies, according the Forrestor Research report, 2016.
1.Predictive analytics: These are software and/or hardware solutions that help firms to discover, assess, optimize, and deploy predictive models through
analysis of big data sources for improving business performance or risk mitigation.
2.NoSQL databases: key-value, document, and graph databases.
3.Search and knowledge discovery: These tools and technologies support self-service extraction of information and development of new insights from
large repositories of structured and unstructured data residing in multiple sources like file systems, streams, databases, APIs, and other applications.

2. 4.Stream analytics: This is a software that can filter, combine, enrich, and analyze a high throughput of data in any
format from multiple disparate live data sources.
5.In-memory data fabric: This provides low-latency access and processing of large amount of data by distributing data
across the dynamic random access memory (DRAM), Flash, or SSD of a distributed computer system.
6.Distributed file stores: It is a computer network that can store data on more than one node, often in a replicated
way, for redundancy and performance.
7.Data virtualization: This technology delivers information from many data sources that include big data sources like
Hadoop and distributed data stores in real-time and near-real time.
8.Data integration: These are the tools for data orchestration across different solutions such as Amazon Elastic
MapReduce (EMR), Apache Pig, Apache Hive, Apache Spark, MapReduce, Hadoop, Couchbase, and MongoDB.
9.Data preparation: This software lessens the burden of sourcing, shaping, cleansing, and sharing diverse and
disordered data sets to help data usefulness for analytics.
10.Data quality: Data quality products conduct data cleansing and augmentation on large, high-velocity sets of data,
using parallel operations on distributed data stores and databases.

3. • Predictive analytics help firms to discover, assess, optimize, and deploy predictive models through analysis of
big data sources.
• Search and knowledge discovery support self-service extraction of information and development of new
insights from large sources of structured and unstructured data.
• Stream analytics can filter, combine, enrich, and analyze a high throughput of data in any format.
• Data virtualization delivers information from many data sources in real-time and near-real time.
• Data integration are the tools for data orchestration across different solutions.
• Published by Brainware University