Big Data Technology 2020- Top Big Data Technologies that you Need to know -

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Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.

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Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”


Types Of Big Data —

BigData’ could be found in three forms:



Any data that can be stored, accessed and processed in the form of fixed format is termed as a ‘structured’ data.

However, nowadays, we are foreseeing issues when a size of such data grows to a huge extent, typical sizes are being in the rage of multiple zettabytes.

Unstructured —
Any data with unknown form or the structure is classified as unstructured data. In addition to the size being huge, un-structured data poses multiple challenges in terms of its processing for deriving value out of it.


Semi-structured data can contain both the forms of data. We can see semi-structured data as a structured in form but it is actually not defined with e.g. a table definition in relational DBMS.

Characteristics of Big Data —

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Volume refers to the unimaginable amounts of information generated every second from social media, cell phones, cars, credit cards, M2M sensors, images, video, and whatnot. We are currently using distributed systems, to store data in several locations and brought together by a software Framework like Hadoop.


Compared to the traditional data like phone numbers and addresses, the latest trend of data is in the form of photos, videos, and audios and many more, making about 70% of the data to be completely unstructured.


Value is the major issue that we need to concentrate on. It is not just the amount of data that we store or process.


Last but never least, Velocity plays a major role compared to the others, there is no point in investing so much to end up waiting for the data.

Applications of Big Data
Big Data is considered the most valuable and powerful fuel that can run the massive IT industries.

Applications of
Big Data is considered the most valuable and powerful fuel that can run the massive IT industries.

  • Travel and Tourism is one of the biggest users of Big Data Technology.
  • Financial and Banking Sectors extensively uses Big Data Technology. Big data analytics can aid banks in understanding customer behaviour based on the inputs received from their investment patterns, shopping trends, motivation to invest and personal or financial backgrounds.
  • Big Data has already started to create a huge difference in the healthcare sector.
  • Government and Military also use Big Data Technology at a higher rate. You can consider the amount of data Government generates on its records and in the military, a normal fighter jet plane requires to process petabytes of data during its flight.

Advantages of Big Data
Big Data Technology has given us multiple advantages, Out of which we will now discuss a few.

Big Data has enabled predictive analysis which can save organisations from operational risks.

Predictive analysis has helped organisations grow business by analysing customer needs.

Big Data has enabled many multimedia platforms to share data Ex: youtube, Instagram

Medical and Healthcare sectors can keep patients under constant observations.

Big Data changed the face of customer-based companies and worldwide market.

Top Big Data Skills -

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Analytical Skills
Data Visualization Skills
Familiarity with Business Domain and Big Data Tools
Skills of Programming
Problem Solving Skills
SQL — Structured Query Language
Skills of Data Mining
Familiarity with Technologies
Familiarity With Public Cloud and Hybrid Clouds
Skills from Hands-on experience.

Required Skills To Become A Big Data Engineer -

Big Data Frameworks/Hadoop-based technologies:

With the rise of Big Data in the early 21st century, a new framework was born. That is Hadoop! All thanks to Doug Cutting, for introducing a framework which not only stores Big Data in a distributed manner but also processes the data parallelly.

For a Big Data Engineer, mastering Big Data tools is a must -

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HDFS (Hadoop Distributed File System):

As the name suggests, it is the storage part of Hadoop, which stores the data in a distributed cluster.


YARN performs resource management by allocating resources to different applications and scheduling jobs.


MapReduce is a parallel processing paradigm which allows data to be processed parallelly on top of Distributed Hadoop Storage i.e. HDFS.


Hive is a data warehousing tool on top of HDFS. Hive caters professionals from SQL background to perform analytics. Whereas Apache Pig is a high-level scripting language which is used for data transformation on top of Hadoop.

Flume & Sqoop:

Flume is a tool which is used to import unstructured data to HDFS whereas Sqoop is used to import & export structured data from RDBMS to HDFS.


Zookeeper acts as a coordinator among the distributed services running in Hadoop environment. It helps in configuration management and synchronizing services.


Oozie is a scheduler which binds multiple logical jobs together and helps in accomplishing a complete task.

Real-time processing Framework (Apache Spark):

Real-time processing with quick actions is the need of the hour. Either it is a credit card fraud detection system or it is a recommendation system,

Apache Spark is a distributed real-time processing framework. It can be easily integrated with Hadoop leveraging HDFS. You can refer to Edureka’s Hadoop & Spark videos to gain comprehensive knowledge.

Database architectures:

One of the most prominent data sources are databases. It is critically important for a Data Engineer to understand database design & database.

SQL-based technologies (e.g. MySQL):

Structured Query Language is used to structure, manipulate & manage data stored in databases. ,

PL/SQL is also prominently used in the industry. PL/SQL provides procedural programming features on top of SQL.

NoSQL technologies :

As the requirements of organizations has grown beyond structured data, so NoSQL databases were introduced.

Some of the most prominently used databases are:

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HBase is column-oriented NoSQL database on top of HDFS It is good for applications with optimized read & range based scan. It provides CP(Consistency & Partitioning) out of CAP.

Cassandra- is a highly scalable database with incremental scalability. . It good for applications with fast & random, read & writes. It provides AP(Available & Partitioning) out of CAP.

MongoDB -

is a document-oriented NoSQL database which is schema-free, i.e. your schema can evolve as the application grows.

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