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Big Data And The Internet Of Things

Posted on December 8, 2015October 18, 2019 by Dexlab

The data that is derived from the Internet of Things may easily be used to make analysis and performance of equipment as well as do activity tracking for drivers and users with wearable devices. But provisions in IT need to be significantly increased.Intelligent Mechatronic Systems(IMS) collects on an average data points no fewer than 1.6billion on a daily basis from automobiles in Canada and U.S.

The data is collected from hundreds of thousands of cars that have on board devices tracking acceleration, the distance traversed, the use of fuel as well as other information related to the operation of the vehicle.This data is then used as a means of supporting insurance programs that are based on use.Christopher Dell, IMS’s senior director recently stated they they were aware that the data available were of value, but what was lacking is the knowledge on how to utilize it.

But in the August of 2015, after a project that lasted for a year, IMS added to its arsenal a NoSQL database with Pentaho providing tools related to data integration and analytics. This lets the data scientists of the company increased flexibility to format the information. This enables the team of analytics to make micro analysis of the driving behavior of customers so that trends and patterns that might potentially enable insurers to customize the rates and policies based on usage.

In addition to this the company further is pursuing an aggressive growth policy through asmartphone app which will further enhance its abilities to collect data from vehicles and smart home systems making use of the Internet of Things.Similar to the case of IMS, organizations that look forward to analyze and collect data gathered from the IoT or the Internet of Things but often find that they need an upgrade of their IT architecture. This principle applies to enterprise as well as consumer sides of the IoT divide.

The boundaries of business increasingly fade away as data is gathered from fitness trackers, diagnostic gears, sensors used in industries, smartphones. The typical upgrade includes updating to big data management technologies like Hadoop, the processing engine Spark,NoSQL databases in addition to advanced tools of analytics with support for applications drivenby algorithms. In other cases all it is needed for the needs of data analytics is the correct combination of IoT data.

Join DexLab Analytics’ Big Data certification course and kick start your career in the rapidly developing sector of data science.

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Hadoop adopted, but not for Analytics?

Posted on November 23, 2015April 11, 2016 by Dexlab

In this era of Big Data, where we are dealing with wide variety of data having features like velocity,volume, veracity and unstructured data coming in from different sources in different formats like xml, logs, videos, images both structured and unstructured.

Everyone is talking about Hadoop’s capabilities in Big Data Analytics, fact is Hadoop is mostly being adopted for low cost Data Storage and ETL.

Continue reading “Hadoop adopted, but not for Analytics?”

Sure shot Ways to Crack Big Data Interviews

Posted on November 11, 2015May 23, 2020 by Dexlab

If you are a Big Data analyst looking for open position in the entry to mid level range of experience then you should prepare yourself with the following resources in your arsenal before you storm an interview with all guns blazing.

Adequate Expertise of Analytical tools like SAS for the processing of data

Make sure that you assign most of the time you have set aside for the preparation of your upcoming interview to brush up your knowledge regarding the tools of analytics that are relevant in your context. Ensure that you acquire proficiency in the analytics tool of your choice. For positions of junior levels the importance of expertise with a particular analytical tool like Hadoop, R or SAS cannot be overstressed. In such circumstances the focus centers around data preparation and processing. It is highly advisable that you review concepts related to the import and manipulation of data, the ability to read data even if it not standard say for example data whose input file types are multiple in number and mixed data formats. You also get to show off your skills at efficiently joining multiple datasets, selecting conditionally the observations or rows of data, how to go about heavy duty data processing of which SQL or macros are the most critical.

Make a Proper Review of End to End Business Process

This is most relevant towards candidates who have prior experience at working in the Big Data and Analytics industry. Prior experience inevitably gives rise to interviewers wanting to know more about the responsibilities that you shouldered and your role in the business process and how you fitted in the context of the broader picture. You should be able to convey to the interviewer that the data source is understood by you along with its processing and use.

A solid concept of the rudiments of statistics and algorithms

Again this tip is also for those with prior experience. Recruiters seek to know whether you are aware of issues likely to be faced by you while you confront problems regarding data and business. Even freshers are expected to know the fundamental concepts of statistics like rejection criteria, hypothesis testing outcomes, measures of model validation and the statistics related assumptions that a candidate must know about in order to implement algorithms of various sorts. In order to crack the interview you must be prepared with adequate knowledge of concepts related to statistics.

Prepare Yourself with At Least 2 Case Studies related to Business

The person on the other side of the interview table will undoubtedly try to make an assessment about your knowledge as far as business analytics is concerned and not solely to the proficiency you command in your tool of choice. Devote time to review projects on analytics you already have worked on if you have prior experience. Be prepared to elucidate on the business problem, the steps that were involved in the processing of data and the algorithm put into use in the creations of the models and reasons behind, and the way the results of the model was implemented. The interviewer might also ask about the challenges faced by you at any stage of the whole process, so keep in mind the issues faced by you in the past and their eventual resolution.

Make Sure that Your Communication Remains Effective

If you are unable to effectively communicate then no much diligent preparations you make, they will be of no use. You can try out mock interviews and answering questions that the recruiter might ask. Spare yourself of the trouble of framing effective answers at the moment when the question is asked during an interview. Though you perhaps will be unable to anticipate each and every question, nevertheless but prior preparation will result in better and more coherent answers.

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The Pros and Cons of HIVE Partitioning

Posted on November 7, 2015May 23, 2020 by Dexlab

Hive organizes data using Partitions. By use of Partition, data of a table is organized into related parts based on values of partitioned columns such as Country, Department. It becomes easier to query certain portions of data using partition.

Partitions are defined using command PARTITIONED BY at the time of the table creation.

We can create partitions on more than one column of the table. For Example, We can create partitions on Country and State.

Syntax:

CREATE [EXTERNAL] TABLE table_name (col_name_1 data_type_1, ….)

PARTITIONED BY (col_name_n data_type_n , …);

Following are features of Partitioning:

It’s used for distributing execution load horizontally.
Query response is faster as query is processed on a small dataset instead of entire dataset.
If we selected records for US, records would be fetched from directory ‘Country=US’ from all directories.

Limitations:

Having large number of partitions create number of files/ directories in HDFS, which creates overhead for NameNode as it maintains metadata.
It may optimize certain queries based on where clause, but may cause slow response for queries based on grouping clause.

It can be used for log analysis, we can segregate the records based on timestamp or date value to see the results day wise / month wise.

Another use case can be, Sales records by Product –type , Country and month.

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HIVE – User Defined Functions

Posted on October 27, 2015October 27, 2015 by Dexlab

Though, Hive has a list of built in functions, in some scenarios we need user defined functions to be written in Java for some specific use cases.

We can use two interfaces which can be used to write UDFs for apache Hive.

The simple API (apache.hadoop.hive.ql.exec.UDF) can be used as long as our function reads and returns primitive types. Means, basic Hadoop & Hive writable types – Text, LongWritable, IntWritable and DoubleWritable etc.

If you plan to write a UDF that deals with embedded data structures, such asList, Mapand Set, then you need to useapache.hadoop.hive.ql.udf.generic.GenericUDF, which is a little more involved.

Simple API – apache.hadoop.hive.ql.exec.UDF
Complex API – apache.hadoop.hive.ql.udf.generic.GenericUDF

Steps to create Hive-UDF

Step 1:-

Open your Eclipse then create a java Class Name

Step 2:-

Add Jar files to project folder

Step 3 :-

Extend UDF Abstract Class

public class classname extends UDF and you return the value.

Step 4 :-

Implement evaluate() method . This method is called once for every row of data being processed

Step 5:-

Compile and create jar file.

Step 6:-

Add jar file to hive class path.

In hive terminal – add jar <jar file path>

Step 7 :-

Create temporary function in Hive Terminal.

CREATE temporary function Convert as ‘udf.Convert′;

udf represents the package name and Convert represents the program name .

For example:

packageudf

importorg.apache.hadoop.hive.ql.exec.UDF;

importorg.apache.hadoop.io.Text;

publicclassConvertextends UDF{

private Text result =new Text();

public Text evaluate(String str){

int number;

number=Integer.parseInt(str);

float fno=(float) number;

String res=Float.toString(fno);

result.set(res);

return result;

}

Here, We have extended UDF abstract class.

This code converts Int to Float.

Assuming a hive table Demo contains column ID with following data:

Select Convert(ID) from Demo gives following output :

1.0

2.0

3.0

5.0

Top 10 Best Hadoop EBooks That You Should Start Reading Now

Posted on October 12, 2015May 23, 2020 by Dexlab

Based on Java, Hadoop is a free open source framework for programming where dealings with huge amounts of processed data in a computing environment is said to be distributed. None other than the Apache Software Foundation is sponsoring it. If you are looking for information about Hadoop, you will like to get in-depth information about the framework and its associated functions. To get you up to the mark with the concepts, the eBooks listed below will prove to be of invaluable help.

MapReduce

If you are looking forward to get started with Hadoop, and maximize your knowledge about Hadoop clusters, this book is of right fit. The book is loaded with information on how t o effectively use the framework to scale apps of the tools provided by Hadoop. This ebook lets you get acquainted with the intricacies of Hadoop with instructions provided on a step-by-step basis and guides you from being a Hadoop newbie to efficiently run and tackle complex Hadoop apps across a large number of machine clusters.

Also read: Big Data Analytics and its Impact on Manufacturing Sector

Programming Pig

If you are looking for a reference from which you may learn more about Apache Pig, which happens to be the engine powering executions of parallel flows of data on the Hadoop framework which also is open source, the Programming Pig is meant for you. Not only does it serve the interests of new users but also provides advanced users coverage on the most important functions like the “Pig Latin” scripting language, the “Grunt” shell and the functions defined by users for extending Pig even further. After reading this book, analyzing terabytes of data is a far less tedious task.

Also read: What Sets Apart Data Science from Big Data and Data Analytics

Professional Hadoop Solutions

This book covers a gamut of topics such as that how to store data with Hbase and HDFS, processing the data with the help of MapReduce and data processing automation with Oozie. Not limiting to that the book further covers the security features of Hadoop, how it goes along with Amazon Web Services, the best related practices and how to automate in real time the Hadoop processes. It provides code examples in XML and Java and refers to them in-depth along with what has been added to the Hadoop ecosystem of late. The eBook positions itself as comprehensive resource with API coverage and exposition of the deeper intricacies, which allow developers and architects to better customize and leverage them.

Apache Sqoop cookbook

This guide allows the user to use Sqoop from Apache with emphasis on application of parameters that are enabled by the Command Line Interface when dealing with cases that are used commonly. The authors offer Oracle, MySQL as well as PostgreSQL examples of databases on GitHub that lend themselves to be easily adapted for Netezza, SQL Server, Teradata etc relational systems.

Also read: Why Getting a Big Data Certification Will Benefit Your Small Business

Hadoop MapReduce Cookbook

The preface of the book claims that the book enables readers to know how to process complex and large datasets. The book starts simple but still gives detailed knowledge about Hadoop. Further, the book claims to be a simple guide on getting things done in one place. It consists of 90 recipes that are offered simply and in a straightforward manner, coupled with systematic instructions and examples from the real world.

Also read: How to Code Colour Values Within SAS Enterprise Guide

Hadoop: The Definitive Guide, 2nd Ed

If you want to know how to maintain and build distributed systems that are both scalable and reliable within the framework of Hadoop then this book is for you. It is intended for – programmers who want to analyze datasets, irrespective of size; and – administrators, who seek to know the setting up and running of Hadoop Clusters, alike. New features like Sqoop, Hive as well as Avro are dealt with in the new second edition. Case studies are also included that may help you out with specific problems.

Also read: How to Use PUT and %PUT Statements in SAS: 6 Tips

MapReduce Design Pattern

If one is to go by the book’s preface, the book is a blend of familiarity and uniqueness. The book is dedicated to design patterns by which we refer to the general guides or templates for solving problems. It is however more open-ended in nature than a “cookbook” as problems are not specified. You have to delve more in the subject matter than mere copying and pasting, but a pattern will get you covered about 90% of the whole way regardless of the challenge at hand.

Also read: SAS Still Dominates the Market After Decades of its Inception

Hadoop Operations

This book is necessary for those who seek to maintain complex and large clusters of Hadoop. Map Reduce, HDFS, Hadoop Cluster Planning. Hadoop Installation as well as Configuration, Authorization and authentication, Identity, Maintenance of clusters and management of resources are all dealt in it.

Programming Hive

Knowledge on programming in Hive provides an SQL dialect in order to query data, which is stored in HDFS, which makes it an indispensable tool at the hands of Hadoop experts. It also works to integrate with other file systems, which may be associated with Hadoop. Examples of such file systems may be MapR-FS and the S3 from Amazon as well as Cassandra and HBase.

Hadoop Real World Solutions CookBook

The preface of this eBook illustrates its use. It lets developers get acquainted and become proficient at problem solving in the Hadoop space. The reader will also get acquainted with varied tools related to Hadoop and the best practices to be followed while implementing them. The tools included in this cookbook are inclusive of Pig, Hive, MapReduce, Giraph, Mahout, Accumulo, HDFS, Ganglia and Redis. This book intends to teach readers what they need to know to apply Hadoop knowledge to solve their own set of problems.

So, happy reading!

DexLab Analytics Presents #BigDataIngestion

Besides, feeding knowledge through eBooks, it is vital to be enrolled for an excellent Big data hadoop certification in Gurgaon. DexLab Analytics is here for you; it offers a gamut of high-end big data hadoop training in Delhi, courses that will surely hone your data skills.

Interested in a career in Data Analyst?
To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.
To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Interested in a career in Data Analyst?

Adequate Expertise of Analytical tools like SAS for the processing of data

Make a Proper Review of End to End Business Process

A solid concept of the rudiments of statistics and algorithms

Prepare Yourself with At Least 2 Case Studies related to Business

Make Sure that Your Communication Remains Effective

Interested in a career in Data Analyst?

Syntax:

Following are features of Partitioning:

Limitations:

Interested in a career in Data Analyst?

MapReduce

Also read: Big Data Analytics and its Impact on Manufacturing Sector

Programming Pig

Also read: What Sets Apart Data Science from Big Data and Data Analytics

Professional Hadoop Solutions

Also read: How To Stop Big Data Projects From Failing?

Apache Sqoop cookbook

Also read: Why Getting a Big Data Certification Will Benefit Your Small Business

Hadoop MapReduce Cookbook

Also read: How to Code Colour Values Within SAS Enterprise Guide

Hadoop: The Definitive Guide, 2nd Ed

Also read: How to Use PUT and %PUT Statements in SAS: 6 Tips

MapReduce Design Pattern

Also read: SAS Still Dominates the Market After Decades of its Inception

Hadoop Operations

Also read: Things to judge in SAS training centres

Programming Hive

Hadoop Real World Solutions CookBook

DexLab Analytics Presents #BigDataIngestion

Interested in a career in Data Analyst?

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