Overview of Big data Hadoop administration and Hadoop architecture

 In this article let us see about big data Hadoop admin and Hadoop architecture. Big data is a concept defined by Structured & Unstructured for a large amount of data. Structured data refers to highly structured data such as in a Relational Hadoop Database(RDBMS) where information is stored in tables.

Structured data can be quickly searched by the end user or search engine. In comparison, unstructured data doesn't fit into conventional RDBMS rows & columns. Types of unstructured data include email, images, audio files, web sites, social media posts, etc. let us see about big data characteristics,More info go through hadoop administration course

Big Data characteristics

Big Data characteristics can be described by the following characteristics

● Volume: The volume of data in big data matters. You're going to work with massive quantities of unstructured data which are small in quality. Data volumes can vary from Terabytes to Petabytes. Scale defines whether the data can be considered as Big Data or Not.

● Velocity: It is the speed at which data is obtained and processed. Big Data is also in real time.

● Variety: It refers to several types of data, both structured and unstructured i.e. Audio, video,

pictures, text messages. This helps to create a concrete result for a person who analyzes big

data.

● Veracity: refers to data quality of the information collected which affects the accuracy of the

analysis.

Wide collection of data sets which can not be stored on a single computer. Big data is huge volume,

rapid pace, and numerous information assets needing an creative framework for enhanced insights and

decision-making.

Big Data (Big Data) & Solution (Hadoop)

Big Data is huge data above the petabyte that is poorly or less organized. This data can not be

completely understood by a person.

Hadoop is the most common Big Data platform on demand that solves Big Data issues.

Here is the Apache Software Foundation's timeline for Hadoop

What is Hadoop?

Hadoop is an open-source platform that allows large data to be stored and processed through simple

programming models in a distributed environment through computer clusters.

Hadoop is designed to scale up to thousands of machines from single servers, each providing local

computing and storage.

Hadoop Architecture Components

Hadoop architecture has three main components:

● Hadoop Distributed File System (HDFS),

● Hadoop MapReduce and

● Hadoop Yarn

A) Data Storage: Hadoop Distributed File System (HDFS):

It is a distributed file system offering high-throughput access to data from applications.

B) Data Processing-Hadoop MapReduce:

This is a YARN-based framework for massive dataset parallel processing. In fact, the term MapReduce refers to the following two separate tasks performed by Hadoop programs: the Map Task: this is the first task to take input data and transform it into a collection of data where individual elements are split into tuples (key / value pairs).

The Reduce Task:

This function takes the results as input from a map function and combines certain data tuples into a

smaller collection of tuples. The reduction function is often carried out after the map function

C) Scheduling & Resource Management- > Hadoop YARN: It is a system for job scheduling and management of cluster resources.

HDFS Architecture

HDFS Architecture Name Node and Data Node are the principal components of HDFS.

Name Node in Hadoop architecture

The Name nodes are maintained and operated by the master daemon. This records all the files

contained in the cluster metadata, e.g. location of the contained blocks, file size, permissions, hierarchy,

etc. The data itself is stored on Data Nodes. We are responsible for helping clients read and writing

messages. They are also responsible for block formation, block deletion and replication, based on the

Name Node decisions.

Data Node in Hadoop architecture

Data Node controls the status of an HDFS node and communicates with blocks. DataNode can perform

CPU-intensive tasks such as semantic and language analysis, statistics and machine learning tasks, and I /O-intensive tasks such as clustering, data import, data export, search, decompression and indexation.

For data processing and conversion a Data Node requires a lot of I / Os.

Each Data Node connects to the Name Node upon startup and performs a handshake to verify the

DataNode's namespace ID and software version. If either of them fails, the Data Node must immediately shut down. A Data Node verifies the possession of block replicas by submitting a block report to the Name Node. The first block report will be submitted, as soon as the Data Node registers. Data Node sends a heartbeat to the Name Node every 3 seconds to confirm the operation of the DataNode and the replicas of the block it hosts.

MapReduce in Hadoop Architecture Implementation

The essence of Hadoop's distributed computing platform is its Hadoop MapReduce programming model based on java. Map or Reduce is a particular form of guided acyclic graph that can be applied to a wide variety of cases of business use. Map function converts the piece of data into pairs of key-value and then the keys are sorted where a reduction function is applied to combine the key-based values into a single output.

Hadoop MapReduce

The execution of a MapReduce job begins when the client submits to the Job Tracker the work

configuration defining the map, combining and reducing functions together with the location for input

and output data. The task tracker determines the number of splits depending on the input direction

after obtaining the task setup, and chooses Task Trackers depending on their network proximity to the

data sources. Job Tracker sends a submission to Project Trackers picked.

The Map Phase processing starts where the Mission Tracker collects the input data from the splits. Map

function is invoked by the "InputFormat" that produces key-value pairs in the memory buffer for each record being parsed. The memory buffer is then sorted by invoking the combine function to different nodes of the reducer. Task Tracker notifies the Work Tracker once the map task is complete. The Job Tracker notifies the selected Task Trackers to begin the reduction process once all Task Trackers are completed. Task Tracker reads files from the area and selects the key value pairs for each key. Then the reduction function is invoked which gathers the aggregated values into the output register.

Hadoop Architecture Design: Best Practices

● Use good-quality commodity servers to make it cost-effective and scalable to scale for complex

business cases. For Hadoop architecture one of the best configurations is to start with 6 core

processors, 96 GB of memory and 1 0 4 TB of local hard drives. This is just a decent setup but not

a full one.

● For data processing to be faster and more effective, transfer the processing closely to the data

instead of separating the two.

● Hadoop scales and performs better with local drives so using Only a Bunch of Disks (JBOD) with

replication rather than a redundant array of separate disks (RAID).

● Develop the multi-tenancy Hadoop architecture by sharing the capacity of the computer with

the load scheduler and sharing HDFS storage.

● Do not edit the metadata files as this can corrupt the Hadoop cluster state.

Conclusion:

I hope you reach a conclusion about Hadoop architecture administration. You can learn more about

Hadoop architecture from big data hadoop training

Hadoop Ecosystem : Learn the Fundamental Tools and Frameworks

 Hadoop is a platform that, using parallel and distributed processing, manages big data storage. Hadoop consists of different methods and mechanisms, such as storing, sorting, and analyzing, dedicated to various parts of data management. Hadoop itself and numerous other associated Big Data instruments are protected by the Hadoop ecosystem.

We will talk about the Hadoop ecosystem and its different fundamental resources in this article. We see a diagram below of the whole Hadoop ecosystem,More info go through big data hadoop course Blog

Hadoop

Let’s start with the Distributed File System ( HDFS) for Hadoop.

HDFS

All data was stored in a single central database under the conventional approach. A single database was not enough to manage the job, with the rise of big data. The solution was to use a distributed strategy to store a large volume of data. Data was distributed to several different databases and split up. HDFS is a specially built file system for storing enormous datasets on commodity hardware, storing data on various machines in different formats.

HDFS comprises two components.

• Name Node-The master daemon is Name Node. Just one active Name Node exists. The Data Nodes are maintained and all metadata stored.
• Data Node-The slave daemon is a data Node. Multiple Data Nodes may exist. The actual data is processed.

So, we spoke about HDFS storing data in a distributed way, but did you know that there are some requirements for the storage system? HDFS splits the data into several blocks, with a limit of 128 MB by default. Depending on the processing speed and the data distribution, the default block size may be modified.

We have 300 MB of data, as seen from the above image. This is broken down to 128 MB, 128 MB, and 44 MB, respectively. The final block handles the remaining storage space necessary, so it doesn’t have to be 128 MB in size. In HDFS, this is how data is processed in a distributed way.

It is also important for you to understand what it sits on and how the HDFS cluster is handled now that you have an overview of HDFS. YARN is doing it, and this is what we’re looking at next.

YARN (Yet Another Negotiator of Resources)

YARN is an acronym for Yet Another Negotiator for Capital. It manages the node cluster and serves as the resource management unit of Hadoop. RAM, memory, and other resources are allocated by YARN to various applications.

There are two components to YARN.

• Resource Manager (Master)-The master daemon is here. It handles resource allocation such as CPU, memory, and bandwidth of the network.
• Node Manager (Slave)-This is the slave daemon and reports to the Resource Manager about the use of the resource.

Let us move on to MapReduce, the processing unit of Hadoop.

Map Reduce

The processing of Hadoop data is based on MapReduce, which processes large quantities of data in a simultaneously distributed way. We can understand how MapReduce functions with the aid of the figure below.

As we see, to finally achieve a production, we have our big data that needs to be processed. So the input data is broken up at the beginning to form the input splits. The first stage is the Map step, where data is passed to generate output values in each split. The mapping step’s output is taken and grouped into blocks of related data in the shuffle and sort process. The output values from the shuffling stage are eventually aggregated. It then returns a single value of the output.

To sum up, the three components of Hadoop are HDFS, MapReduce, and YARN. Let us now, begin with Sqoop, dive deep into the data collection and ingestion tools.

Sqoop

Sqoop is used to move data, such as relational databases and business data warehouses, between Hadoop and external datastores. It imports data into HDFS, Hive, and HBase from external data stores.

The client machine gathers code, as shown below, which will then be sent to Sqoop. The Sqoop then goes to the Task Manager, which, in turn, connects to the warehouse of enterprise data, systems based on documents, and RDBMS. It can map these assignments into Hadoop.

Flume

Flume is another method for data collection and ingestion, a distributed service for vast volumes of log data collection, aggregation, and movement. It ingests social media, log files, web server data into HDFS online streaming.

As you can see below, information, depending on the needs of your organization, is taken from different sources. The source, channel, and sink are then passed through it. The sink function ensures that the specifications are in line with everything. Finally, it dumps the data into HDFS.

Now let’s take a look at the scripting languages and query languages of Hadoop.

Pig

Yahoo researchers created Apache Pig, aimed primarily at non-programmers. It was designed to analyze and process data sets without the use of complicated Java codes. This offers a high-level language for data processing that can conduct multiple tasks without being bogged down by too many technical terms.

It is comprised of:

• Pig Latin-This is the scripting language for
• Pig Latin Compiler-Translates the code from Pig Latin to executable code

Extract, Pass, and Load (ETL) and a forum for building data flow are also supported by Pig. Did you know that ten Pig Latin script lines are equivalent to around 200 MapReduce work lines? To analyze datasets, Pig utilizes quick, time-efficient steps. Let’s take a closer look at the architecture of The Pig.

Latin-pig

To analyze data using Pig, programmers write scripts in Pig Latin. Grunt Shell is the virtual shell of Pig, which is used to run all Pig scripts. If a Pig script is written in a script file, it is executed by the Pig Server. The Pig script syntax is tested by the parser, after which the output will be a DAG (Directed Acyclic Graph). To the logical optimizer, the DAG (logical plan) is transmitted. The DAG is translated into MapReduce jobs by the compiler. The Execution Engine then runs the MapReduce work. The results are shown using the “DUMP” statement and stored using the “Inventory” statement in HDFS.

Hive is next up on the language list.

The Hive

To facilitate the reading, writing, and management of large datasets residing in distributed storage, Hive utilizes SQL (Structured Query Language). As users were familiar with writing queries in SQL, the hive was built with a vision of combining the concepts of tables and columns with SQL.

There are two main components of the Apache Hive.

• Control Line of Hive
• Driver for JDBC/ ODBC

The application for Java Database Connectivity (JDBC) is connected via the JDBC driver, and the application for Open Database Connectivity (ODBC) is connected via the ODBC Driver. In CLI, commands are executed directly. For all the queries sent, the Hive driver is responsible for performing the three stages of internal compilation, optimization, and execution. To process questions, it then uses the MapReduce system.

The Hive architecture is presented below:

Spark

In and of itself, Spark is an immense platform, an open-source distributed computing system for the collection and analysis of massive real-time data volumes. It runs faster than MapReduce 100 times. Spark offers an in-memory data calculation that is used, among other items, to process and analyze real-time streaming data such as stock market and banking data.

The Master Node has a driver program, as seen from the above picture. As a driver application, the Spark code works and generates a SparkContext, which is a portal to all Spark functionalities. Spark applications operate on the cluster as separate sets of processes. Inside the cluster, the driver software and Spark background take care of the job execution. A job is divided into several tasks spread over the worker node. It can be spread across different nodes when an RDD is generated in the Spark sense. Worker nodes are slaves with various tasks going. The completion of these activities is the responsibility of the Executor. Worker nodes perform Cluster Manager assigned tasks and return the results to the Spark Context.

Let us now switch to the Hadoop Machine Learning area and its numerous permutations.

Mahout

Mahout is used to constructing machine learning algorithms that are scalable and distributed, such as clustering, linear regression, classification, and so on. It has a library for collaborative filtering, grouping, and clustering that includes built-in algorithms.

Ambari

We’ve got Apache Ambari next up. It is an open-source tool that keeps track of the applications running and their status. Hadoop clusters are managed, tracked, and provisioned by Ambari. Also, to start, stop, and configure Hadoop services, it offers a central management service.

The Ambari Site, which is your interface, is linked to the Ambari server, as shown in the following image. Apache Ambari meets an architectural master/slave. To keep track of the state of the infrastructure, the master node is responsible. The master node uses a database server that can be configured during the period of configuration to do this. The Ambari server is most frequently located on the MasterNode and linked to the database. This is where the host server is looked at by officers. Agents run on all the nodes under Ambari that you want to control. Occasionally, this program sends heartbeats to the master node to demonstrate its aliveness. Ambari Server is able to perform several tasks by using Ambari Agent.

Conclusion

I hope you reach to a conclusion about the Hadoop Ecosystem and tools. You can learn more through big data online training

Hadoop - HDFS Architecture

 

NameNode and DataNodes

HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction from the NameNode,More info go through hadoop admin course

The DataNode are pieces of software designed to run on commodity machines and Name Node run on High Availability Hardware. These machines typically run a GNU/Linux operating system (OS). HDFS is built using the Java language; any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software. The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case.

The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The NameNode is the arbitrator and repository for all HDFS metadata. The system is designed in such a way that user data never flows through the NameNode.

The File System Namespace

HDFS supports a traditional hierarchical file organization. A user or an application can create directories and store files inside these directories. The file system namespace hierarchy is similar to most other existing file systems; one can create and remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features.

The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is recorded by the NameNode. An application can specify the number of replicas of a file that should be maintained by HDFS. The number of copies of a file is called the replication factor of that file. This information is stored by the NameNode.

Data Replication

HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time.

The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode.

Replica Placement: The First Baby Steps

The placement of replicas is critical to HDFS reliability and performance. Optimizing replica placement distinguishes HDFS from most other distributed file systems. This is a feature that needs lots of tuning and experience. The purpose of a rack-aware replica placement policy is to improve data reliability, availability, and network bandwidth utilization. The current implementation for the replica placement policy is a first effort in this direction. The short-term goals of implementing this policy are to validate it on production systems, learn more about its behavior, and build a foundation to test and research more sophisticated policies.

Large HDFS instances run on a cluster of computers that commonly spread across many racks. Communication between two nodes in different racks has to go through switches. In most cases, network bandwidth between machines in the same rack is greater than network bandwidth between machines in different racks.

The NameNode determines the rack id each DataNode belongs to via the process outlined in Hadoop Rack Awareness. A simple but non-optimal policy is to place replicas on unique racks. This prevents losing data when an entire rack fails and allows use of bandwidth from multiple racks when reading data. This policy evenly distributes replicas in the cluster which makes it easy to balance load on component failure. However, this policy increases the cost of writes because a write needs to transfer blocks to multiple racks.

For the common case, when the replication factor is three, HDFS’s placement policy is to put one replica on one node in the local rack, another on a node in a different (remote) rack, and the last on a different node in the same remote rack. This policy cuts the inter-rack write traffic which generally improves write performance. The chance of rack failure is far less than that of node failure; this policy does not impact data reliability and availability guarantees. However, it does reduce the aggregate network bandwidth used when reading data since a block is placed in only two unique racks rather than three. With this policy, the replicas of a file do not evenly distribute across the racks. One third of replicas are on one node, two thirds of replicas are on one rack, and the other third are evenly distributed across the remaining racks. This policy improves write performance without compromising data reliability or read performance.

The current, default replica placement policy described here is a work in progress.

Replica Selection

To minimize global bandwidth consumption and read latency, HDFS tries to satisfy a read request from a replica that is closest to the reader. If there exists a replica on the same rack as the reader node, then that replica is preferred to satisfy the read request. If angg/ HDFS cluster spans multiple data centers, then a replica that is resident in the local data center is preferred over any remote replica.

Safemode

On startup, the NameNode enters a special state called Safemode. Replication of data blocks does not occur when the NameNode is in the Safemode state. The NameNode receives Heartbeat and Blockreport messages from the DataNodes. A Blockreport contains the list of data blocks that a DataNode is hosting. Each block has a specified minimum number of replicas. A block is considered safely replicated when the minimum number of replicas of that data block has checked in with the NameNode. After a configurable percentage of safely replicated data blocks checks in with the NameNode (plus an additional 30 seconds), the NameNode exits the Safemode state. It then determines the list of data blocks (if any) that still have fewer than the specified number of replicas. The NameNode then replicates these blocks to other DataNodes.

The Persistence of File System Metadata

The HDFS namespace is stored by the NameNode. The NameNode uses a transaction log called the EditLog to persistently record every change that occurs to file system metadata. For example, creating a new file in HDFS causes the NameNode to insert a record into the EditLog indicating this. Similarly, changing the replication factor of a file causes a new record to be inserted into the EditLog. The NameNode uses a file in its local host OS file system to store the EditLog. The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the FsImage. The FsImage is stored as a file in the NameNode’s local file system too.

The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. This key metadata item is designed to be compact, such that a NameNode with 4 GB of RAM is plenty to support a huge number of files and directories. When the NameNode starts up, it reads the FsImage and EditLog from disk, applies all the transactions from the EditLog to the in-memory representation of the FsImage, and flushes out this new version into a new FsImage on disk. It can then truncate the old EditLog because its transactions have been applied to the persistent FsImage. This process is called a checkpoint. In the current implementation, a checkpoint only occurs when the NameNode starts up. Work is in progress to support periodic checkpointing in the near future.

The DataNode stores HDFS data in files in its local file system. The DataNode has no knowledge about HDFS files. It stores each block of HDFS data in a separate file in its local file system. The DataNode does not create all files in the same directory. Instead, it uses a heuristic to determine the optimal number of files per directory and creates subdirectories appropriately. It is not optimal to create all local files in the same directory because the local file system might not be able to efficiently support a huge number of files in a single directory. When a DataNode starts up, it scans through its local file system, generates a list of all HDFS data blocks that correspond to each of these local files and sends this report to the NameNode: this is the Blockreport.

The Communication Protocols

All HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to a configurable TCP port on the NameNode machine. It talks the ClientProtocol with the NameNode. The DataNodes talk to the NameNode using the DataNode Protocol. A Remote Procedure Call (RPC) abstraction wraps both the Client Protocol and the DataNode Protocol. By design, the NameNode never initiates any RPCs. Instead, it only responds to RPC requests issued by DataNodes or clients.

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Data Validation Framework in Apache Spark for Big Data Migration Workloads

 In Big Data, testing and assuring quality is the key area.

However, data quality problems may destroy the success of many Data Lake, Big Data, and ETL projects. Whether it’s a big data or small, the need for the quality data doesn’t change. Moreover, high-quality data is the perfect driver to get insights from it. The data quality is measured based on the business satisfaction by deriving the necessary insights,More info go through big data online course

Steps included in the Big Data validation.

• Row and Column count

• Checking Column names

• Checking Subset Data without Hashing

• Statistics Comparison- Min, Max, Mean, Median, 25th, 50th, 75th percentile

• SHA256 Hash Validation on entire data

Debugging

When there is a mismatch between source and sink, then we should know how to find out particular corrupt data within the entire data which may include 3000+ columns and Millions of records.

Let’s discuss the same by looking into the columns that come in the way.

Context

Under Big Data, we have transferred the data from MySQL to Data Lake. Moreover, the quality of the data has to be verified before it is ingested by downstream applications.

For example purpose, I have gone with a sample customer data (having 1000 records) within Spark Dataframe. However, the demo is with a small amount of data, this solution can be scaled to the enormous data volume.

Plot-1

Thus, the same data exists within two Dataframes, so our Data validation framework will be a green signal.

I have intentionally changed the data in the previous records in the 2nd data frame. So, we can see how this hash validation framework helps.

Let’s see the steps in the data validation process as this is the core part of this validation in Big Data.

Step-01: Row and Column count

This check will happen in a typical data migration pipeline.

Step-02: Checking Column Names 

The following check will make sure that we don’t have corrupt or additional columns in this Big Data validation.

Step-03: Checking Subset Data without Hashing

This type of checking is like fruit to fruit comparison. Moreover, it means that this will validate the actual data (Big Data) without applying the hash function. But, this checking has a limitation up to a few records only, as this may consume more resources if we do it using Big data or huge data.

Step-04: Statistics Comparison using— (Min, Max, Mean, Median, 25th, 50th, 75th percentile)

In rare cases, there can be a collision or attack in hash validation. This may lead to data corruption. Moreover, this can be evaded by calculating statistics on each column in the data.

Step-05: SHA256 Hash Validation on entire data

For this example, I have chosen SHA256 but there are some other hashing algorithms also available as well such as MD5.

Hash Function

Hashing algorithms or functions are useful to generate a fixed-length result (the hash, or hash value) from the given input. Moreover, this hash value is an abstract of the actual data.

EX:

• Hash function                                                            

Good boy        

• Hash Value

2debae171bc5220f601ce6fea18f9672a5b8ad927e685ef902504736f9a8fffa

The above example explains the function hash and its related hash value under the SHA256 algorithm. A small change in the character can change the total hash value.

This kind of technique is extremely used in digital signatures, authentication, indexing Big Data in hash tables, detecting duplicates, etc. Moreover, this technique is useful to detect whether a sent file didn’t suffer any accidental or intentional data corruption.

Verifying a Hash

In Big Data, data can be compared to a hash value to ascertain its integrity. Typically, data is hashed at a precise time and the corresponding hash value is secured in some way. At a later time, the data can be divided again and compared to the secured value. In case, the hash value matches, the data has not been modified. If the value does not match, it means the data has been corrupted or faulted. For this system to work, the secured hash or division must be encrypted or kept secret from all suspicious parties.

Hash Collision

A collision or attack occurs when two different keys include the same hashCode. This may happen as two unlike objects within Java can have a similar hashCode. Moreover, a Hash Collision is an attempt to determine two input strings of a function hash that gives the same hash result. Because these functions have immense input length and a predefined output length. Thus, there is automatically going to be the possibility of two distinct inputs that give the same output hash as a result.

When we have millions of records and 3000+ columns, it becomes hard to compare the source and destiny system for data mismatch in Big Data. For doing this, we need a big memory and calculation power engines. To address this, we are using Hashing to link all the 30k+ columns together in a chain into one single hash value column. This just includes 64 characters in length. This amount is unimportant while comparing the 30k+ column length and size.

Why Data migration is important in Big Data?

Nonetheless of the actual purpose of data migration in Big Data, the goal is generally to enhance performance and competitiveness.

Anyhow, we have to get it right!

When the migration is less successful, it results in incorrect data that includes dismissals and unknowns. This may happen even when the source data is completely usable and appropriate. Furthermore, any issues that do exist in the source data can be turned up while bringing it into a new, more advanced system.

A complete Big Data migration strategy averts a sub-parallel experience that ends up developing more issues than it resolves. Apart from missing deadlines and exceptional budgets, incomplete plans can cause migration projects to fail completely. In planning and strategizing the work, teams need to give complete attention towards migrations, instead of making them secondary to another project with a big scope.

A strategic data migration plan should include the reflection of the following important factors:

Knowing the data —

Before performing the migration, all source data needs a complete audit check. Unexpected issues may occur in case this step is ignored.

Cleanup:

Once the user recognizes any problem with the source data, they must be resolved in less time. Moreover, this work may require additional software tools and third-party resources due to the large scale work.

Maintenance and security:

Data undergoes humiliation after some time, making it untrustworthy. This means there must be the maintenance of data quality by placing controls.

Governance:

It becomes necessary to track and report on data quality because it allows a better understanding of data honesty. Furthermore, the processes and tools used to generate this information should be highly useful and do automated functions where possible.

Final Thought

Thus, we have discussed how a framework in Spark for Big Data migration validates data. However, poor data quality will put a burden on the working team's quality time in fixing them. I hope, this article will help to address the data quality problems after migration from source to destination using Spark. Get more insights from big data online training