10 Most Effective Big Data Technologies

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All you Need to know about:
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Big Data Technologies

 Big data is simply large amount of data
 Data can be structured or unstructured
 Data is analysed to gain insights
 Insights help in making better business decisions
 Helps management to take tactical decisions regarding the future of their business
What is Big Data?

 Big data technologies help organizations take out meaningful information out of
 gigabytes of data.
 They are important tools to organize and analyse large volumes of data.
 Many big data technologies are available today, invented by the tech leaders of the
 world.
Big Data Technologies

 A part of advanced analytics, which makes predictions about what will happen in
future.
 Analysis is done through technology.
 Uses various techniques like statistics, modelling, artificial intelligence, machine
learning, data mining and others.
 All these methods are used to predict the uncertainties of business.
 Helps organizations to improve business performance and mitigate risk.
Top 10 Big Data Technologies
1. Predictive Analytics

 Often known as Non-SQL, Non-relational or not, only SQL database.
 Provides a platform for businesses to store and retrieve data by finding relationships
between various data groups.
 First introduced in the late 1960s.
 Gained popularity in the 21st century, when companies like Facebook, Google,
Amazon started using them.
 Simple in design and provides better control over available solutions.
2. NoSQL Databases

 Collects information and knowledge from various sources like databases, file
systems, streams, APIs, etc
 Delivers useful insights based on a specific context or need
 Information is collected through extensive search in these platforms and intelligent
search results are delivered. Simple and usable technology
3. Search and Knowledge Discovery

 An event processing engine where the event can be fed in real-time from a single
stream of data or multiple streams.
 Data is then filtered and analyzed to get multiple outputs which can be used in
business.
 Data can be fed from multiple sources also like applications, devices, sensors,
operation system, websites, etc
4. Stream Analytics

 An infrastructure software that slides between applications and data sources.
 Processes enormous amounts of data very fast to deliver real time results.
 Extremely good for high-performance transactions, real-time streaming,
comprehensive data processing and a variety of other tasks.
 In most cases, distributes data between dynamic random access memory (DRAM),
Flash and SSD of a computer system.
5. In-memory Data Fabric

 A technology where data is stored in various layers which are often similar in structure
 Ensures higher performance and lower redundancy
 In most cases, location agnostic which help to improve reliability and reduce the
complexity of different layers of data.
6. Distributed File Stores

 A technique by which data is retrieved and manipulated
 Does not take into account the technical details about the data like where is it located
or how was it found.
 Data is processed in real time or near real time basis.
7. Data Virtualization

 Data are combined using technical and business intelligence.
 Processed from different sources to bring out meaningful information.
 Information output is often termed as ‘trusted’ because of the way the same is
processed.
 This method uses solutions like Amazon Elastic Map Reduce (EMR), Apache Hive,
Apache Spark, Couch base, Hadoop etc.
8. Data Integration

 Manipulates data and makes it suitable for further processing and analysis.
 Manipulation is done through various tasks, both manual and automated.
 The preparation work is tedious, time-consuming and routine.
 Main intention is to reduce the burden of shaping and cleaning a diverse set of high
volume data for the analysts.
9. Data Preparation

 Refers to cleansing a large volume of data to ensure the output is of high quality.
 The high-quality data can then be used for operations, decision making, and planning.
10. Data Quality

 Big Data is here to stay.
 It’s growing faster than ever.
 By 2020, 1.7 megabytes of new information will be created every second of
every human on earth (Prediction).
 Total accumulated data today is 44 zettabytes. It will grow ten times by 2020.
 Every second 40,000 search queries are made on Google.
Future Roadmap

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10 Most Effective Big Data Technologies

10 Most Effective Big Data Technologies

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