PostgreSQL Installation on Ubuntu

There are multiple ways of installation of the PostgreSQL database on Unix like platforms. We will explore the installation of PostgreSQL version 11 on Ubuntu 18.04 LTS using apt installation. Let’s start. Login to Ubuntu and check the version of Ubuntu. postgres@sanjeeva:/home/sanjeeva/postgres$ lsb_release -a No LSB modules are available. Distributor ID: Ubuntu Description: Ubuntu 18.04.5 LTS Release: Read more about PostgreSQL Installation on Ubuntu[…]

Evolvement of PostgreSQL: Background

Research papers of “System R” from IBM were initially picked up by two professors, Michael Stonebraker and Eugene Wong, at Berkeley University, California. This resulted in a new database called INteractive Graphics REtrieval System i.e. “Ingres“. The work done by this duo for Ingres becomes the foundation of many relational databases like MS SQL Server, Read more about Evolvement of PostgreSQL: Background[…]

How To: List all indexes in MongoDB

Indexes are a very important structure in any database and the same is true with MongoDB as well. It adds spices to the performance of your query. It is also very important to know what and how many indexes are created on your DB. In the normal life of DBA, one needs to find out Read more about How To: List all indexes in MongoDB[…]

How To: Creating a collection subset from collection in MongoDB

Many a time there are required to create a new collection from existing ones with the same or different size. In this blog, we will see how to create a new collection from the existing one and with a smaller size. For Sample data, We may use Kaggle to get huge data set. In my collection, I Read more about How To: Creating a collection subset from collection in MongoDB[…]

Cloud database war: Advantage shifting to Red?

With its inception in 2006, Amazon AWS has definitely gone a long way.  Engineers from Amazon worked really-really well which has not only completely changed the horizon of cloud but also emerges as one of the boon for any business to adopt.  Although, there are many other vendors available in the cloud market and they Read more about Cloud database war: Advantage shifting to Red?[…]

WiredTiger: A game changer for MongoDB

Storage engine is one of the key component of any database.  It is, in fact, a software module which is used by database management system to perform all storage related operations e.g. create information, read information and update any information.  The term storage means both disk storage and memory storage.   Choosing right storage engine is Read more about WiredTiger: A game changer for MongoDB[…]

Data Pump: impdp

Problem Statement: Restore entire database using Data Pump. Restore table(s) Restore tablespace(s) Restore schema(s) Restore using Transportable tablespaces (TTS) Restore from multiple small sizes of dump files Restore in parallel mode Approach: There are single shot solution to all the above problem statement and it is IMPDP in Data Pump.  It is one of various Read more about Data Pump: impdp[…]

Oracle Data Pump: expdp & impdp

Problem Statement: Backup entire database using Data Pump. Backup table(s) Backup tablespace(s) Backup schema(s) Backup using Transportable tablespaces (TTS) Generate multiple small sizes of dump files Backup in parallel mode Approach: There are single shot solution to all the above problem statement and it is Data Pump.  It is one of various backup tools provided Read more about Oracle Data Pump: expdp & impdp[…]

MongoDB Installation – Ubuntu

MongoDB is one of the document oriented open source database developed in c++, first come into shape in 2007 when in order to overcome the shortfall of existing database while working for an advertising company “DoubleClick” development team has decided to go further rather than struggling with database.  The team of this advertising company was Read more about MongoDB Installation – Ubuntu[…]

How to copy Multi terabyte data to another Database

Problem statement:  How to migrate huge data from One DB to another DB. Multi-Terabyte data loaded on one database should be copied to another database. Environment: You have multi-terabyte Database Your database is growing on daily basis, based on data feeds. Number of Indexes on these tables are very high, and thus, size of indexes Read more about How to copy Multi terabyte data to another Database[…]

MapReduce Unwinding … Reduce

Once this shuffling completed, it is where REDUCE come into action.  Its task is to process the input given by SHUFFLE into the output so that user can understand what is the result of the file processed by hadoop. After shuffling completed, it is clear that one word will be processed by only one DN and not Read more about MapReduce Unwinding … Reduce[…]

MapReduce Unwinding … Sort & Shuffle

This is in continuation of MapReduce Processing …… This output will be input for next process which is SORT. Sort takes this [L<K,V>] and sorts all the words in order of alpha bates (a to z) on each DN. Sorted arrangement on DNs : DN -1: NODE – 1 [ L (K, V)] PKT-1(K) V Read more about MapReduce Unwinding … Sort & Shuffle[…]

MapReduce Unwinding. . . . . Map

In last discussion on MapReduce, we discussed the algorithm which is used by Hadoop for data processing using MapReduce. Now its time to understand this in detail with help of an example. Lets consider our scenario : We have 7 Node cluster where 1 Node is Name Node (NN) and rest of 6 node is Read more about MapReduce Unwinding. . . . . Map[…]

MapReduce Unwinding. . . . . .Algorithm

With discussion, in my last blog, about “How Hadoop manages Fault Tolerance” within its cluster while processing data, it is now time to discuss the algorithm which MapReduce used to process these data. It is Name Node (NN) where a user submits his request to process data and submits his data files.  As soon as NN receives data Read more about MapReduce Unwinding. . . . . .Algorithm[…]

MapReduce Unwinding. . . . . . Fault Tolerance

Before we see the intermediate data produced by the mapper, it would be quite interesting to see the fault tolerant aspects of Hadoop with respect to MapReduce processing. Once Name node (NN) received data files which has to be processed, it splits data files to assign it to Data Node (DN).  This assignment would be Read more about MapReduce Unwinding. . . . . . Fault Tolerance[…]

MapReduce Unwinding. . . . . Philosophy

The philosophy of Map Reduce workings is straight forward and can be summarized in 6 steps. Whatever data we provide as input to Hadoop, it first splits these data into smaller no of pieces. Typically, the size of data splitted is limited to 64MB.  If a file of 1 TB is arrived to process on data node, Read more about MapReduce Unwinding. . . . . Philosophy[…]

MapReduce : Internals

The MapReduce Framework: MapReduce is a programming paradigm that provides an interface for developers to map end-user requirements (any type of analysis on data) to code. This framework is one of the core components of Hadoop. The capabilities: The way it provides fault-tolerant and massive scalability across hundreds or thousands of servers in a cluster Read more about MapReduce : Internals[…]


Disadvantage of DWH: Because of the limitation of currently available Enterprise data warehousing tools, Organizations were not able to consolidate their data at one place to maintain faster data processing. Here comes the magic of hadoop for their rescue. Traditional ETL tools may take hours, days and sometimes even weeks.  And because of this, performances Read more about MAGIC OF HADOOP[…]

Journey of Hadoop

History of Hadoop: At the outset of twenty-first century, somewhere 1999-2000, due to increasing popularity of XML and JAVA, internet was evolving faster than ever. This leads to the invention of Hadoop. Requirement is mother of invention: As the world wide web grew at dizzying pace, though current search engine technologies were working fine, a Read more about Journey of Hadoop[…]

Big Data: An Introduction

Innovations in technologies made the resources cheaper than earlier.  This enables organizations to store more data at lower cost and thus increasing the size of data. Gradually the size of data becomes bigger and now it moves from Megabytes (MB) to Petabytes (1e+9 MB). This huge increase in data requires some different kind of processing.  Read more about Big Data: An Introduction[…]

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