Top Big Data Tools to Store Data in Data Processing Cycle

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Welcome To Loginworks Softwares
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Top Big Data Tools to Store Data in Data
Processing Cycle

• Data processing can be understood as the
  conversion of raw data into a meaningful and
  desired form. Basically, producing information
  that can be understood by the end user. So then,
  the question arises, what is raw data and why
  does it need conversion? Raw data can be any fact
  or figure, for example, the number of hours an
  employee has worked in a month, his rate of pay
  etc. all these are numbers or facts, the
  meaningful information that comes out of it after
  processing is the employees payroll or an
  invoice. This is the most common reference for
  data processing. The term is closely related to
  specialist business tasks like sales order
  processing or sales ledger processing, graphics,
  charts etc. Therefore, much of the information
  used in an organization is provided by data
  processing systems.

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Data processing can be achieved in several ways.
The method that must be deployed for it depends
on certain factors like

1. Size and the nature of the business

2. Timing facet
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Data processing is divided into 6 stages, and we
receive the output in the final stage. These
stages are

1. Collection of data

2. Storage of data

3. Data Sorting

4. Data Processing

4. Data Analysis

6. Presentation and Conclusions

For any logical operations or calculations to be
performed on data, first all the required facts
must be collected. This data is then stored and
processed. This task has seen several
advancements in the types of tools that can be
used to accomplish it.

Present day organization needs

In todays digital world, Electronic Data
Processing is the most popular technique of data
processing. Organizations hold a large amount of
data or Big Data to be processed for their
functioning. Traditional applications or tools
are becoming obsolete for processing this massive
amount of data as they are incapable of handling
it. Most organizations process data exceeding
Terabytes in size. Several challenges are faced
in processing this amount of data that is also
diverse and complex to handle. It has been
observed through surveys in organizations that
almost 80 of the data collected in an
unstructured format. To produce the relevant
output after data processing, most relevant and
important data must be captured. However, with
the unstructured large volume of data, this task
gets extremely complicated and unattainable. Then
comes the issue of storage of this massive amount
of data.
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Modern technology has sufficed the situation
through present day tools developed for the
storage and analysis of Big Data.

Tools to store and analyze data in Data
Processing

1. Apache Hadoop

Apache Hadoop is an open-source software
framework based on java capable of storing a
great amount of data in a cluster. It can process
large sets of data in parallel across clusters of
computers. The concept is to scale up from a
single server to several thousands of machines,
each with a capability to perform local
computation and provide storage. It eliminated
the dependency on hardware for delivering
high-availability. The detection and handling of
failures are possible through the library at the
application layer.

Apache Hadoop offers below modules

Hadoop Common This module consists of the
utilities to support other modules.

Hadoop Distributed File System (HDFS)
High-throughput access to the application data is
provided by the distributed file system of
Hadoop.

Hadoop YARN cluster resource management and job
scheduling are achieved by this framework.

Hadoop MapReduce It involves parallel processing
of large sets of data or Big data.

Hadoop Distributed File System (HDFS) is the main
storage system of Hadoop. The HDFS splits the
large data sets across several machines to be
processed in parallel. There is also replication
of data in a cluster, performed by HDFS, thus,
enabling high availability of data.
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Cassandra This scalable multi-master database
does not allow any single points of failure.

Chukwa Large distributed systems require
management which is achieved by a data collection
system called Chukwa.

HBase HBase provides the capability of
structured data storage through a distributed and
scalable database for large tables.

Hive Hive is a data warehouse that provides the
capability of data summarization and ad hoc
querying.
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2. Microsoft HDInsight
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Azure HDInsight is Microsofts cloud-based
solution for extremely quick, easy and
cost-effective data processing on a large scale.
HDInsight utilizes Windows Azure Blob storage as
the default file storage system. This cloud
service is capable of providing high availability
of data at low cost. Multiple scenarios like Data
warehousing, ETL, Machine Learning and IoT are
enabled through it.

HDInsight uses the most commonly used open-source
frameworks, for example, Spark, Hadoop, Hive,
Storm, Kafka etc. Microsoft HDInsight is a highly
effective analytics service for organizations and
enterprises which is fully-managed and
full-spectrum.

The service has increased in its popularity for
being cost-effective with additional fifty
percent prices cut on HDInsight. This obviously
inclines enterprises to switch to the cloud.
Organizations have highly benefited through the
HDInsight service which has proved itself capable
of satisfying their primary needs. The system is
highly secure, with enterprise-grade protection
through encryption and meets the essential
compliance standards like PCI, HIPPA, ISO etc.
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3. NoSQL

NoSQL (Not Only SQL) database has come up with
the ability to handle unstructured data when the
traditional SQL could only handle large sets of
structured data. There is no particular schema in
NoSQL databases to support unstructured data.
This was quite a boost for the enterprises
incorporating regular updates to their
applications, in achieving the flexibility to
handle them quickly. There can be a wide variety
of data models, which may include key-value,
document, graph and columnar formats. Better
performance is achieved through NoSQL when the
amount of data is to be stored. It enables
large-scale data clustering in web applications
and cloud. Several NoSQL DBs are available in the
present day to be able to analyze Big Data.

Data storage is achieved through

Key-value stores stores each piece of data or
value associated with a unique key. Examples of
implementations include Aerospike, Memchache DB,
Berkeley DB, Riak, Redis etc.

Document Databases stores semi-structured data
and metadata in a document format. Example,
MongoDB, MarkLogic, CouchDB, DoucumentDB etc.

Wide-column stores the data is stored in the
data tables organized as columns instead of rows.
Example, Cassandra, Google BigTable, HBase etc.

Graph stores stores data in the form of nodes,
just like records in an RDBMS. The connections
between the nodes are called edges. Example,
Allegro graph, Neo4j, IBM Graph, Titan etc.
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Features of NoSQL include

Offers on-demand, pay as you go system

Auto-healing and Seamless upgrades

Flexible to scale up and down as required

Customizable and deep monitoring alert system

Very secure and reliable

Backup and recovery options available

4. Hive

Hive is a data warehouse built on top of Apache
Hadoop, facilitating reading, writing and
management of large datasets. The dataset resides
in a distributed storage system.It is managed
using SQL-like query option HiveSQL (HSQL). HSQL
is used to analyze and query Big Data. The
primary use of Hive is for Data mining.

Its features include
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5. Sqoop

Apache Sqoop is a tool designed to connect Hadoop
with numerous relational databases in order to
transfer data. It is capable of transferring a
large amount of data between structured data
stores and Hadoop effectively and efficiently.

Advantages of Sqoop

Sqoop facilitates the transfer of data between
various types of structured databases like
Postgres, Oracle, Teradata, and so on.

Offloading of certain processing done in the ETL
is possible through Sqoop since the data resides
in Hadoop. It is a cost-effective, efficient and
fast method of carrying out Hadoop processes.

Data transfer via Sqoop can be executed in
parallel among many nodes.

6. PolyBase

Polybase technology can access data from outside
the database through t-SQL. PolyBase works on top
of SQL Server 2012 Parallel Data Warehouse (PDW)
and it accesses data stored in PDW. It can
execute queries on external Hadoop data or to
import or export Azure Blob storage data.
PolyBase is extremely useful for organizations to
make lucrative decisions on data. This is
achieved by bridging the gap of data transfer
between different data sources like structured
relational databases and unstructured Hadoop
data. There is no need for installation of any
external software to your Hadoop environment to
achieve this. Knowledge of Hadoop is not required
by end users to query the external tables.
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PolyBase Features

It can query the data from SQL Server or PDW
stored in Hadoop. Since the data is distributed
in different distributed storage systems like
Hadoop for scalability, PolyBase enables us to
query that data easily through t-sql.

It can query the data that is stored inside Azure
Blob Storage. Data used by Azure services is
securely stored and managed in Azure Blob
storage. PolyBase technology is an effective and
efficient medium to work on it through t-sql.

It can import Hadoop data and data stored in
Azure Blob Storage or Azure Data Lake Store.
PolyBase works on Microsoft SQLs columnstore
technology to perform analysis on data imported
from Hadoop, Azure data lake store or Azure Blob
store without the need of performing ETL
operations.

It can export data to Hadoop, Azure Data Lake
Store or Azure Blob Storage. As Hadoop and Azure
storage systems provide cost-effective methods
for data storage, PolyBase provides the
technology to export data to these systems and
archive it.

Integration with BI tools. Integration with
Microsofts Business Intelligence tools and other
analysis tools is also possible with
PolyBase.
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Performance of PolyBase

Push computation to Hadoop. The query optimizer
used in PolyBase makes a cost-based decision
after which computations are pushed to Hadoop to
considerably improve the performance. This
decision is based on statistics. This creates
MapReduce jobs and utilizes Hadoops distributed
resource.

Scales compute resources. The use of SQL Server
PolyBase scale-out groups improves the query
performance tremendously. Parallel data transfer,
therefore, becomes possible between different SQL
server instances and the nodes in Hadoop.
External data computation also leverages extra
compute resources.

7. Big data in EXCEL 2013

Excel has been popular with many users and
organizations, who find comfort in using it more
than other complicated software. Considering this
Microsoft has come up with a tool that can
connect data stored in Hadoop, which is EXCEL
2013. Hortonworks primarily provides Enterprise
Apache Hadoop, which gives an option to access
big data using EXCEL 2013stored in their Hadoop
platform. The Power View feature of EXCEL 2013
can be easily used to summarize data in Hadoop.

Excel 2013 provides the ability to perform the
exploratory or ad-hoc analysis. Excel is popular
with data analysts who prefer using traditional
tools to get rich data insights by interacting
with new types of data stores. The Data Model
feature of Excel 2013 supports large volumes of
data for organizational usage.
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