#xenonstack

Overview

In the era of the Internet of Things and Mobility, with a huge volume of data becoming available at a fast velocity, there must be the need for an efficient Analytics System.

Also, the variety of data is coming from various sources in different formats, such as sensors, logs, structured data from an RDBMS, etc. In the past few years, the generation of new data has drastically increased. More applications are being built, and they are generating more data at a faster rate.

Earlier, Data Storage was costly, and there was an absence of technology which could process the data in an efficient manner. Now the storage costs have become cheaper, and the availability of technology to transform Big Data is a reality.

What is Big Data Concept?

According to the Author Dr. Kirk Borne, Principal Data Scientist, Big Data Definition is Everything, Quantified, and Tracked. Let’s pick that apart -

Everything – Means every aspect of life, work, consumerism, entertainment, and play is now recognized as a source of digital information about you, your world, and anything else we may encounter.

Quantified – Means we are storing those "everything” somewhere, mostly in digital form, often as numbers, but not always in such formats. The quantification of features, characteristics, patterns, and trends in all things is enabling Data Mining, Machine Learning, statistics, and discovery at an unprecedented scale on an unprecedented number of things. The Internet of Things is just one example, but the Internet of Everything is even more impressive.

Tracked – Means we don’t directly quantify and measure everything just once, but we do so continuously. It includes - tracking your sentiment, your web clicks, your purchase logs, your geolocation, your social media history, etc. or tracking every car on the road, or every motor in a manufacturing plant or every moving part on an airplane, etc. Consequently, we see the emergence of smart cities, smart highways, personalized medicine, personalized education, precision farming, and so much more.

Advantages of Streaming Data

Smarter Decisions

Better Products

Deeper Insights

Greater Knowledge

Optimal Solutions

Customer-Centric Products

Increased Customer Loyalty

More Automated Processes, more accurate Predictive and Prescriptive Analytics

Better models of future behaviors and outcomes in Business, Government, Security, Science, Healthcare, Education, and more.

D2D Communication Meets Big Data

Data-to-Decisions

Data-to-Discovery

Data-to-Dollars

10 Vs of Big Data

Big Data Architecture & Patterns

The Best Way to a solution is to "Split The Problem." Big Data Solution can be well understood using Layered Architecture. The Layered Architecture is divided into different Layers where each layer performs a  particular function.

This Architecture helps in designing the Data Pipeline with the various requirements of either Batch Processing System or Stream Processing System. This architecture consists of 6 layers which ensure a secure flow of data.

Data Ingestion Layer

This layer is the first step for the data coming from variable sources to start its journey. Data here is prioritized and categorized which makes data flow smoothly in further layers.

Data Collector Layer

In this Layer, more focus is on the transportation of data from ingestion layer to rest of data pipeline. It is the Layer, where components are decoupled so that analytic capabilities may begin.

Data Processing Layer

In this primary layer, the focus is to specialize the data pipeline processing system or we can say the data we have collected in the previous layer is to be processed in this layer. Here we do some magic with the data to route them to a different destination, classify the data flow and it’s the first point where the analytic may take place.

Data Storage Layer

Storage becomes a challenge when the size of the data you are dealing with, becomes large. Several possible solutions can rescue from such problems. Finding a storage solution is very much important when the size of your data becomes large. This layer focuses on "where to store such a large data efficiently."

Data Query Layer

This is the layer where active analytic processing takes place. Here, the primary focus is to gather the data value so that they are made to be more helpful for the next layer.

Data Visualization Layer

The visualization, or presentation tier, probably the most prestigious tier, where the data pipeline users may feel the VALUE of DATA. We need something that will grab people’s attention, pull them into, make your findings well-understood.

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1. Data Ingestion Architecture

Data ingestion is the first step for building Data Pipeline and also the toughest task in the System of Big Data. In this layer we plan the way to ingest data flows from hundreds or thousands of sources into Data Center. As the Data is coming from Multiple sources at variable speed, in different formats.

That's why we should properly ingest the data for the successful business decisions making. It's rightly said that "If starting goes well, then, half of the work is already done."

What is Ingestion in Big Data?

Big Data Ingestion involves connecting to various data sources, extracting the data, and detecting the changed data. It's about moving data - and especially the unstructured data - from where it is originated, into a system where it can be stored and analyzed.

We can also say that Data Ingestion means taking data coming from multiple sources and putting it somewhere it can be accessed. It is the beginning of Data Pipeline where it obtains or import data for immediate use.

Data can be streamed in real time or ingested in batches, When data is ingested in real time then, as soon as data arrives it is ingested immediately. When data is ingested in batches, data items are ingested in some chunks at a periodic interval of time. Ingestion is the process of bringing data into Data Processing system.

Effective Data Ingestion process begins by prioritizing data sources, validating individual files and routing data items to the correct destination.

Challenges in Data Ingestion

As the number of IoT devices increases, both the volume and variance of Data Sources are expanding rapidly. So, extracting the data such that it can be used by the destination system is a significant challenge regarding time and resources. Some of the other problems faced by Data Ingestion are -

When numerous Big Data sources exist in the different format, it's the biggest challenge for the business to ingest data at the reasonable speed and further process it efficiently so that data can be prioritized and improves business decisions.

Modern Data Sources and consuming application evolve rapidly.

Data produced changes without notice independent of consuming application.

Data Semantic Change over time as same Data Powers new cases.

Detection and capture of changed data - This task is difficult, not only because of the semi-structured or unstructured nature of data but also due to the low latency needed by individual business scenarios that require this determination.

That's why it should be well designed assuring following things -

Able to handle and upgrade the new data sources, technology and applications

Assure that consuming application is working with correct, consistent and trustworthy data.

Allows rapid consumption of data

Capacity and reliability - The system needs to scale according to input coming and also it should be fault tolerant.

Data volume - Though storing all incoming data is preferable; there are some cases in which aggregate data is stored.

Data Ingestion Parameters

Data Velocity - Data Velocity deals with the speed at which data flows in from different sources like machines, networks, human interaction, media sites, social media. The movement of data can be massive or continuous.

Data Size - Data size implies enormous volume of data. Data is generated by different sources that may increase timely.

Data Frequency (Batch, Real-Time) - Data can be processed in real time or batch, in real time processing as data received on same time, it further proceeds but in batch time data is stored in batches, fixed at some time interval and then further moved.

Data Format (Structured, Semi-Structured, Unstructured) - Data can be in different formats, mostly it can be the structured format, i.e., tabular one or unstructured format, i.e., images, audios, videos or semi-structured, i.e., JSON files, CSS files, etc.

Big Data Ingestion Key Principles

To complete the process of Data Ingestion, we should use right tools for that and most important that tools should be capable of supporting some of the fundamental principles written below -

Network Bandwidth - Data Pipeline must be able to compete with business traffic. Sometimes traffic increases or sometimes decreases, so Network bandwidth scalability is biggest Data Pipeline challenge. Tools are required for bandwidth throttling and compression capabilities.

Unreliable Network - Data Ingestion Pipeline takes data with multiple structures, i.e., images, audios, videos, text files, tabular files data, XML files, log files, etc. and due to the variable speed of data coming, it might travel through the unreliable network. Data Pipeline should be capable of supporting this also.

Heterogeneous Technologies and System - Tools for Data Ingestion Pipeline must be able to use different data sources technologies and different operating system.

Choose Right Data Format - Tools must provide data serialization format, that means as data comes in the variable format so converting them into single format will provide an easier view to understand or relate the data.

Streaming Data - It depends upon business necessity whether to process the data in batch or streams or real time. Sometimes we may require both processing. So, tools must be capable of supporting both.

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Data Serialization in Big Data

Different types of users have various types of data consumer needs. Here we want to share variable data, so we must plan how the user can access data in a meaningful way. That's why a single image of variable data optimize the data for human readability.

Approaches used for this are -

Apache Thrift

It's an RPC Framework containing Data Serialization Libraries.

Google Protocol Buffers

It can use the specially generated source code to easily write and read structured data to and from a variety of data streams and using a variety of languages.

Apache Avro

The more recent Data Serialization format that combines some of the best features which previously listed. Avro Data is self-describing and uses a JSON-schema description. This schema is included with the data itself and natively support compression. Probably it may become a de facto standard for Data Serialization.

Big Data Ingestion Tools

Apache Flume

Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.

It has a straightforward and flexible architecture based on streaming data flows. It is robust and faults tolerant with tunable reliability mechanisms and many failovers and recovery mechanisms.

It uses a simple, extensible data model that allows for an online analytic application.

Functions of Apache Flume

Stream Data - Ingest streaming data from multiple sources into Hadoop for storage and analysis.

Insulate System - Buffer storage platform from transient spikes, when the rate of incoming data exceeds the rate at which data can be written to the destination

Scale Horizontally - To ingest new data streams and additional volume as needed.

Apache Nifi

Apache Nifi provides an easy to use, the powerful, and reliable system to process and distribute data. Apache NiFi supports robust and scalable directed graphs of data routing, transformation, and system mediation logic. Its functions are -

Track data flow from beginning to end

Seamless experience in design, control, feedback, and monitoring

Secure because of SSL, SSH, HTTPS, encrypted content.

Elastic Logstash

Elastic Logstash is an open source, server-side data processing pipeline that ingests data from a multitude of sources simultaneously transforms it, and then sends it to your “stash, " i.e., Elasticsearch.

It easily ingests from your logs, metrics, web applications, data stores, and various AWS services and done in continuous, streaming fashion. It can Ingest Data of all Shapes, Sizes, and Sources.

2. Data Collector Architecture

In this Layer, more focus is on transportation data from ingestion layer to rest of Data Pipeline. Here we use a messaging system that will act as a mediator between all the programs that can send and receive messages.

Here the tool used is Apache Kafka. It's a new approach in message-oriented middleware.

Apache Kafka Overview

It is used for building real-time data pipelines and streaming apps. It can process streams of data in real-time and store streams of data safely in a distributed replicated cluster.

Kafka works in combination with Apache Storm, Apache HBase and Apache Spark for real-time analysis and rendering of streaming data.

Data Pipeline Architecture

Data Pipeline the main component of Data Integration. All transformation of data happens in Data Pipeline.

It is a Python-based tool that streams and transforms real-time data to service that need it.

Data Pipeline Automate the movement and transformation of data. Data Pipeline is a Data Processing engine that runs inside your application.

It is used to transform all the incoming data in a standard format so that we can prepare it for analysis and visualization. Data Pipeline is built on Java Virtual Machine (JVM).

So, a Data Pipeline is a series of steps that your data moves through. The output of one step in the process becomes the input of the next. Data, typically raw data, goes on one side, passes through a series of steps.

The steps of a Data Pipeline can include cleaning, transforming, merging, modeling and more, in any combination.

Data Pipeline Functions

Data Ingestion

Data Pipeline Helps in bringing data into your system. It means taking unstructured data from where it is originated into a system where it can be stored and analyzed for making business decisions

Data Integration

Data Pipeline also helps in bringing different types of data together.

Data Organization

Organizing data means an arrangement of data; this arrangement is also made in Data Pipeline.

Data Refining

It's also one of the processes where we can enhance, clean, improve the raw data.

Data Analytics

After improving the useful data, Data Pipeline provides us the processed data on which we can apply the operations on raw data and can make business decisions accurately.

Need Of Data Pipeline

A Data Pipeline is software that takes data from multiple sources and makes it available to be used strategically for making business decisions.

Primarily reasons for the need of data pipeline is because it's tough to monitor Data Migration and manage data errors. Other reasons for this are below -

Business Decisions - Critical Analysis is only possible when combining data from multiple sources. For making business decisions, we should have a single image of all the data coming.

Connections - All the time data keeps on increasing, new data came and old data modified, so, each new integration can take anywhere from a few days to a few months to complete.

Accuracy - The only way to build trust with data consumers is to make sure that your data is auditable. One best practice that’s easy to implement is never to discard inputs or intermediate forms when altering data.

Latency - The fresher your data, the agiler your company’s decision-making can be. Extracting data from APIs and databases in real-time can be difficult, and many target data sources, including large object stores like Amazon S3 and analytics databases like Amazon Redshift, are optimized for receiving data in chunks rather than a stream.

Scalability - Data can be increased or decreased with time we can't say for on Monday data will come less and rest of days comes a lot for processing. So, usage of data is not uniform. What we can do is making our pipeline so scalable that able to handle any amount of data coming at variable speed.

Data Pipeline Use Cases

Data Pipeline is useful to some roles, including CTOs, CIOs, Data Scientists, Data Engineers, BI Analysts, SQL Analysts, and anyone else who derives value from a unified real-time stream of user, web, and mobile engagement data. So, use cases for data pipeline are given below -

For Business Intelligence Teams

For SQL Experts

For Data Scientists

For Data Engineers

For Product Teams

What is Apache Kafka for?

Building Real-Time streaming Data Pipelines that reliably get data between systems or applications

Building Real-Time streaming applications that transform or react to the streams of data.

Apache Kafka Use Cases

Stream Processing

Website Activity Tracking

Metrics Collection and Monitoring

Log Aggregation

Apache Kafka Features

One of the features of Apache Kafka is durable Messaging.

Apache Kafka relies heavily on the file system for storing and caching messages: rather than maintain as much as possible in memory and flush it all out to the filesystem, all data is immediately written to a persistent log on the filesystem without necessarily flushing to disk.

Apache Kafka solves the situation where the producer is generating messages faster than the consumer can consume them in a reliable way.

Apache Kafka Architecture

Apache Kafka System design act as Distributed commit log, where incoming data is written sequentially on disk. There are four main components involved in moving data in and out of Apache Kafka -

Topics - Topic is a user-defined category to which messages are published.

Producers - Producers post messages to one or more topics

Consumers - Consumers subscribe to topics and process the posted messages.

Brokers - Brokers that manage the persistence and replication of message data.

3. Data Processing Layer

In the previous layer, we gathered the data from different sources and made it available to go through rest of pipeline.

In this layer, our task is to do magic with data, as now data is ready we only have to route the data to different destinations.

In this main layer, focus is to specialize Data Pipeline processing system or we can say the data we have collected by the last layer in this next layer we have to do processing on that data.

Batch Processing System  

A simple batch processing system for offline analytics. For doing this tool used is Apache Sqoop.

What is Apache Sqoop?

It efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Apache Sqoop can also be used to extract data from Hadoop and export it into external structured data stores.

Apache Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB.

Functions of Apache Sqoop

Import sequential data sets from mainframe

Data imports

Parallel data Transfer

Fast data copies

Efficient data analysis

Load balancing

Near Real Time Processing System  

A pure online processing system for online analytics. For this type of processing Apache Storm is used.The Apache Storm cluster makes decisions about the criticality of the event and sends the alerts to the warning system (dashboard, e-mail, other monitoring systems).

What is Apache Storm?

It is a system for processing streaming data in real time. It adds reliable real-time data processing capabilities to Enterprise Hadoop. Storm on YARN is powerful for scenarios requiring real-time analytics, machine learning and continuous monitoring of operations.

6 Key Features of Apache Storm

Fast – It can process one million 100 byte messages per second per node.

Scalable – It can do parallel calculations that run across a cluster of machines.

Fault-tolerant – When workers die, Storm will automatically restart them. If a node dies, the worker will be restarted on another node.

Reliable – Storm guarantees that each unit of data (tuple) will be processed at least once or exactly once. Messages are only replayed when there are failures.

Easy to operate – It consists of Standard configurations that are suitable for production on day one. Once deployed, Storm is easy to work.

Hybrid Processing system - This consist of Batch and Real-time processing System capabilities. For this type of processing tool used is Apache Spark and Apache Flink.

What is Apache Spark?

Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs to allow data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to data sets.

With Spark running on Apache Hadoop YARN, developers everywhere can now create applications to exploit Spark’s power, derive insights, and enrich their data science workloads within a single, shared data set in Hadoop.

What is Apache Flink?  

Apache Flink is an open-source framework for distributed stream processing that Provides results that are accurate, even in the case of out-of-order or late-arriving data. Some of its features are -

It is stateful and fault-tolerant and can seamlessly recover from failures while maintaining exactly-once application state.

Performs at large scale, running on thousands of nodes with excellent throughput and latency characteristics.

It's streaming data flow execution engine, APIs and domain-specific libraries for Batch, Streaming, Machine Learning, and Graph Processing.

Apache Flink Use Cases

Optimization of e-commerce search results in real-time

Stream processing-as-a-service for data science teams

Network/Sensor monitoring and error detection

ETL for Business Intelligence Infrastructure

4. Data Storage Layer

Next, the major issue is to keep data in the right place based on usage. We have relational Databases that were a successful place to store our data over the years.

But with the new big data strategic enterprise applications, you should no longer be assuming that your persistence should be relational.

We need different databases to handle the different variety of data, but using different databases creates overhead. That's why there is an introduction to the new concept in the database world, i.e., the Polyglot Persistence.

What is Polyglot Persistence?

Polyglot persistence is the idea of using multiple databases to power a single application. Polyglot persistence is the way to share or divide your data into multiple databases and leverage their power together.

It takes advantage of the strength of different database. Here various types of data are arranged in a variety of ways. In short, it means picking the right tool for the right use case.

It’s the same idea behind Polyglot Programming, which is the idea that applications should be written in a mix of languages to take advantage of the fact that different languages are suitable for tackling different problems.

bitcoin

Overview

There are different kinds of people in the world, who have different languages, different cultures, different eating styles but one of the things that brings them together is money, which is everybody's necessity.

In today’s fast moving and developing era, we need a safe and secure platform to be able to make online transactions, for whatever purpose they might be. In whatever transactions we do, banks are the third party involved in it, and they have all records of our transaction and of one’s account holdings.

Blockchain Technology aims at removing this third-party involvement and keeping the transactions only limited to the sender and the receiver. A broad idea behind this technology was to build a secure platform where safe transactions could happen in a very transparent manner.

With the help of Blockchain technology, we can save ourselves from any kind of overheads and third party involvements and send or receiving money could be as hassle-free as sending emails. BlockChain Technology is a peer to peer software technology that protects a digital piece of information.

What is BlockChain Technology?

The origin of Blockchain technology is a little uncertain and still for experts to find out for sure. It is said to be invented by a person or a group of people known by the name of Satoshi Nakamoto in the year 2009.

Initially, it was developed to enable digital transactions between two parties in an anonymous fashion, without having the need of a third party to perform verification of the transaction.

The main inspiration behind the development of such a great technology at the time was to facilitate the transfer of Bitcoins, but it later caught on and is today being used for many other important things.

The blockchain technology is an open system for transaction processing that follows distributed ledger approach whose goal is to automate the processes and reduce data storage costs and provide data security and eliminate duplicates.

We can also say that BlockChain is a method of recording data, i.e., transactions, contracts, agreements that need to be recorded independently and verified as they are happening.

A very good way to understand the concept of Blockchain technology is the google docs analogy. Just the way Google Docs shared between two people enables both of them to make changes to the document at the same time and visible to the other party, similarly the transparency of blockchain works.

BlockChain sounds like a revolution and is the underlying Technology behind Bitcoin. Truly, BlockChain is a mechanism that brings everyone to the highest degree of accountability, i.e., no more missed transaction, no more third party involvement in the transaction.

Blockchain guarantees the validity of a transaction by recording it not only on the main register but also by connecting distributed system of registers, and all of which are connected through a secure validation mechanism. It may have been invented to create the alternative currency Bitcoin but can be used for other purposes like online signature services, voting systems, and many other applications.

It's a good way instead of sending our payment information through servers, in BlockChain Technology all transactions are Copied and cross-checked between every computer in a system, which becomes very safe at scale.

BlockChain Technology is a type of distributed ledger that means a database that is consensually shared and synchronized across network spread across multiple sites, institutions. Blockchain provides an unalterable, public record of digital transactions in packages called Blocks. These digitally recorded “Blocks” of data are stored in a linear path, and each block contains cryptographically hashed data. Main Features -

Capable of transforming

Making transaction faster

Reducing cost

More security

Transparency

Seamless and simultaneous integration of transaction

Settlements and ledger updates directly between multiple parties

Creates a secure way to share information

Conduct transactions without the need for a single, central party to approve them.

Only authorized network members can see details of their transactions, providing confidentiality and privacy.

All updates to the shared ledger are validated and recorded on all participants shared ledgers, which drives security and accuracy.

All updates to the ledger are unchangeable and auditable. Network members can accurately trace their past activity.

How Does BlockChain Works?

The Blockchain technology enables direct transactions between two parties without any intermediary such as a bank or a governing body. It is essentially a database of all transactions happening in the network.

The database is public and therefore, not owned by any one party, it is distributed that is, it is not stored on a single computer. Instead, it is stored on many computers across the world. The database is constantly synchronized to keep the transactions up to date and is secured by the art of cryptography making it hacker proof.

The basic framework on which the whole blockchain technology works is actually two-fold first is gathering data (transaction records) and the second is putting these blocks together securely in a chain with the help of cryptography.

Say a transaction happens, this transaction information is shared with everybody on the blockchain network. These transactions are individually timestamped and once these transactions are put together in a block, they have timestamped again as a whole block.

Now, this complete block is appended to the chain in the blockchain network. Other participants might also be adding to the network at the same time, but the timestamps added to each block takes care of the order in which the blocks are appended to the network.

The timestamps also take care of any duplicity issues hence, everybody on the network has the recent version of the chain available to them. The main cryptographic element that makes this whole system tamper-free is the hash function.

Each block's information is taken and a hash function is applied to it. The value computed from this is then stored in the next block and so on. So in this way, each block’s hash function value is being carried by the next block in the chain which makes tampering with the contents of the block very difficult.

Even if some changes are made to the block, one could easily find out because that block’s hash value would not be the same as the already calculated value of the hash function that was stored in the next block of the chain.

Blockchain works as a network of computers. Bitcoin photography is used to keep transactions secure and also shared among those in the network after the transaction is validated, the details of the transfer are recorded on a public ledger that anyone on the network and sees in the existing financial system essentially ledger maintained by the institution access the custodian of the information.

But on a blockchain the information is transparently held in a shared database and no one party access the movement, thus increasing the trust among parties.

How Bitcoin Transaction Works?

For everyone, it's easy to download a simple piece of software and install it on the computer. But to use Bitcoin which is a decentralized be out of your system, we do not need to register an account with any particular company or handle or any of your personal details, once you have a wallet you can create addresses which effectively become your identity within the network.

Suppose party A wants to send money to party B in the form of Bitcoins. For both party A and Party B, the transaction is collected in a block. A block record some or all of the most recent Bitcoin transactions that have not yet entered any cry of blocks, the new block is then broadcasted to all the parties or so-called nodes in the network the parties in the network approve that the transaction is valid through a process called mining.

Building Blocks With BlockChain

A very simple definition of blockchain is that “the blockchain is distributed, digital ledger.” One of the key features of the blockchain is that it is a ‘Distributed Database’ that is to say, the database exists in multiple copies across multiple computers.

Concept of BlockChain Technology

Shared View - One of the most powerful features of BlockChain Technology is it’s shared a view of data for all participants in a peer-to-peer network. Transaction records can be shared but cannot be altered. Shared views have two approaches i.e. Traditional Approach where each party maintains their own independent ledger and Blockchain Approach where all parties share and maintain the same ledger.

Cryptography - Cryptography is used to establish identity and protect the integrity of the underlying data. One of its concepts is hashing and this concept is used in the blockchain technology. Hashing is an effective means of determining if any piece of data has been changed or not. It generates a fingerprint for a piece of data by applying a cryptographic function to it. Changing one character in the original string results in a completely different hash value. Also, the original string cannot be reverse engineered from the hash function.

CryptoCurrency - Everything You Need to Know

Cryptocurrency is a virtual currency which uses cryptography for security. The main defining features of this currency which make what it is are its decentralized nature, transparent ledgers and it's security feature which makes it resistant to any kind of malicious manipulation. Bitcoin is the is the first and the most famous cryptocurrency. It was invented in 2009 and has seen an enormous rise in value ever since.

The cryptographic technique that it follows is SHA-256, it is a hash function that is used to encrypt every transaction before being added to the blockchain. The underlying and main aspect on which cryptocurrency works is that all the transactions happen in a transparent and decentralized ledger for everyone to see and verify if need be. One can go back to the very first transaction of a user to verify their credibility by tracking back in the ledger.

Benefits of CryptoCurrencies

The advantages of cryptocurrency are much fold. To name a few would be:

No Fraudulent activities - Transactions happening in this process cannot be reversed or in any other way be tampered with, without at least letting anybody know. The whole architecture is such that any discrepancies cannot go unnoticed and can be detected.

Faster transactions - The traditional way of settling payments involves a trusted third party, namely banks mainly, this makes the process take more time. In cryptocurrency, since the payments are peer-to-peer without any involvement of a third party, the payment process happens immediately without any delay. No Overheads- With the elimination of third party involvements, the overheads in the form of service fees that these institutions charge are also eliminated. This makes cryptocurrencies more cost-effective.

The integrity of identity - Unlike credit cards, which always have a risk of getting misused since all the information regarding it is given to the vendor, this mode of payment does not divulge any more information than required and only pushes the amount of money that is required to be paid.

How is Blockchain Changing Money and Business?

Blockchain technology is likely to have a great impact on next few decades. Currently, Blockchain is not the most thundering concept in the world, but it is believed that it will be the next generation of the internet. For the past few decades, we've had the internet for information.

The crucial difference between Internet and BlockChain is that the Internet enables the exchange of data, blockchain could enable the exchange of value, i.e., it could allow users to carry out trade and commerce across the globe without the need for payment processors, curator, and settlement and adjusting entities. Trust is a very crucial thing, and blockchain is one of the biggest technologies that peer.

Trust enables people everywhere to trust each other and transact peer established, not by some big institution, but by collaboration, by cryptography and by some smart code. And because trust is native to the technology, so we can say that it's “The Trusted Protocol.”

9 Key Features Of BlockChain:

While Scaling up the Business and working with remote teams with a different skill set and culture, I realize the need for processes and automation to improve the productivity and collaboration. 

 At growth Stage, with 3+ Years experience of delivering more than 55 projects in various domains for Startups and Enterprises including -

Mobility

Big Data

Internet of Things

Private Cloud and Hybrid Cloud

Problems Faced By Developers & Operations Team

Ownership Issues during Deployment

Fewer and Slow Releases

Flat Access Control and Security

Revision Control

Scaling Up Resources For Application Stack

Manual Processes Involved in Delivery Pipeline

Isolated Declaration of Dependencies

Single Configuration for Multiple Deployments

Manual Testing Results Into Slower Release

Shared Backing Services

Lean Start To DevOps Adoption

We Started transformation towards DevOps Strategy by adopting processes like Integration of DevOps Tools, Processes and Data into our work Culture. Parallelly, We Started adopting different Infrastructure architectures, Building Private Cloud,  Docker, Apache Mesos, and Kubernetes.

Initial Steps To Implement DevOps

Enforcing Rules with the help of right tools - Agile board integration with SCM, Build Tool and Deployment Tool

Collaboration Tools - Rocket Chat Integration with Taiga, GitLab, Jenkins

Continuous Integration and Delivery

Explicit Dependency Management

Automated Testing

Hands-On Training

We started by creating two separate teams from existing pool of developers to adopt DevOps culture for new Projects in Big Data and Mobile Applications. After Initial hurdles in adaptation to Collaboration Tools and new delivery pipeline,  results came out were extraordinary.

Results After Initial Phase

Improved Performance & Productivity

Less Manual Work

Better Collaboration and Communication

Developers Getting more Empowered and Involved in Delivery

Proper Dependency and Configuration Management

Challenges To DevOps Adoption

Cultural Shift in the way Things were being developed

Changing Mindset for Adaptation.

Support for Legacy Environments

Integrating Security and Compliance with new Setup

No support for Overlay Networks