Data Ingestion, Processing and Architecture layers for Big Data and IoT

Overview of Big Data, Data Ingestion and Processing

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 Technology?

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

Advantages of Big Data 

D2D Communication Meets Big Data

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.

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.

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.

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.

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."

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.

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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Big 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 - 

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

Data Ingestion Parameters

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 -

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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 -

It's an RPC Framework containing Data Serialization Libraries.

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.

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 Architecture

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

Apache Nifi Overview

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 -

Integrating Elasticsearch with 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.

Big Data Pipeline 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.

Getting Started with Big Data Pipeline

Big Data Pipeline Functions

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 Pipeline also helps in bringing different types of data together.

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

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

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

Need Of Big 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 -

Big 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 -

 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.

Apache Kafka Use Cases

Apache Kafka Features

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 -

Big 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, the 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.

Big 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

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

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.

Real-Time Processing System

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 -

Apache Flink Use Cases

Big 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.

Advantages of Polyglot Persistence -

Big Data Storage Tools

HDFS : Hadoop Distributed File System

Features of HDFS

GlusterFS: Dependable Distributed File System

As we know good storage solution must provide elasticity in both storage and performance without affecting active operations.

Scale-out storage systems based on GlusterFS are suitable for unstructured data such as documents, images, audio and video files, and log files. GlusterFS is a scalable network filesystem.

Using this, we can create large, distributed storage solutions for media streaming, data analysis, and other data- and bandwidth-intensive tasks.

GlusterFS Use Cases

Amazon S3 Storage Service

Big Data Query Layer

It is the layer where active analytic processing takes place. This is a field where interactive queries are necessaries, and it’s a zone traditionally dominated by SQL expert developers. Before Hadoop, we had an insufficient storage due to which it takes long analytics process.

At first, it goes through a Lengthy process, i.e., ETL to get a new data source ready to be stored and after that, it puts the data in database or data warehouse. But now, data analytics became essential step which solved problems while computing such a large amount of data.

Companies from all industries use big data analytics to -

Big Data Analytics Query Tools

Apache Hive is data warehouse infrastructure built on top of Apache Hadoop for providing data summarization, ad-hoc query, and analysis of large datasets.

Data analysts use Hive to query, summarize, explore and analyze that data, then turn it into actionable business insight.

It provides a mechanism to project structure onto the data in Hadoop and to query that data using a SQL - like a language called HiveQL (HQL).

Features of Apache Hive

Spark SQL includes a cost-based optimizer, columnar storage, and code generation to make queries fast.

At the same time, it scales to thousands of nodes and multi-hour queries using the Spark engine, which provides full mid-query fault tolerance.

Spark SQL is a Spark module for structured data processing. Some of the Functions performed by Spark SQL are -

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. We use Amazon Redshift to load the data and run queries on the data.

We can also create additional databases as needed by running a SQL command. Most important we can scale it from hundred gigabytes of data to a petabyte or more.

It enables you to use your data to acquire new insights for your business and customers. The Amazon Redshift service manages all of the work of setting up, operating and scaling a data warehouse.

These tasks include provisioning capacity, monitoring and backing of the cluster, and applying patches and upgrades to the Amazon Redshift engine.

Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.

Presto was designed and written for interactive analytics and approaches and the speed of commercial data warehouses while scaling to the size of organizations like Facebook.

Presto Capabilities

Who Uses Presto?

Data Lake and Data Warehouse

What is Data Warehouse?

A Data Warehouse is a subject-oriented, Integrated, Time-varying, non-volatile collection of data in support of management’s decision-making process.

So, a Data Warehouse is a centralized repository that stores data from multiple information sources and transforms them into a standard, multidimensional data model for efficient querying and analysis.

Difference Between Big Data and Data Warehouse

While comparing, we found that a big data solution is a technology and that data warehousing is an architecture. They are two very different things.

Technology is just that – a means to store and manage large amounts of data. A data warehouse is a way of organizing data so that there are corporate credibility and integrity.

When someone takes data from a data warehouse, that person knows that other people are using the same data for other purposes. There is a basis for reconcilability of data when there is a data warehouse.

What is Data Lake?

It is a new type of cloud-based enterprise architecture that structures data in a more scalable way that makes it easier to experiment with it.

With data lake, incoming data goes into the lake in a raw form or whatever form data source providers, and there we select and organize the data in a raw form. There are no assumptions about the schema of the data; each data source can use whatever scheme it likes.

It's up to the consumers of that information to make sense of that data for their purposes. The idea is to have a single store for all of the raw data that anyone in an organization might need to analyze.

Commonly people use Hadoop to work on the data in the lake, but the concept is broader than just Hadoop.

Capabilities of Data Lake

Data Lake vs Data Warehouse

Real-Time Data Monitoring, Data Visualization, Big Data Security

This layer focus on Big Data Visualization. We need something that will grab people’s attention, pull them in, make your findings well-understood. That’s why it provides full Business Infographics. Because your findings from your data need the annotation and the bold canvas.

Data Visualization Layer 

The data visualization layer often is the thermometer that measures the success of the project. This is the where the data value is perceived by the user. While it’s designed for handling and storing large volumes of data, Hadoop and other tools have no built-in provisions for data visualization and information distribution, leaving no way to make that data easily consumable by end business users.

Tools For Building Data Visualization Dashboards

Custom Dashboards for Data Visualization

Custom dashboards are useful for creating unique overviews that present data differently, For example, you can -

Real-Time Visualization Dashboards

Real-Time Dashboards save, share, and communicate insights. It helps users generate questions by revealing the depth, range, and content of their data stores.

Data Visualization with Tableau

Exploring data sets With Kibana

Introduction to Intelligence Agents 

Recommendation Systems

Angular.JS Framework

Understanding React.JS

Useful Features of React

Big Data Security and Data Flow

Security is the primary task of any work. Security should be implemented at all layers of the lake starting from Ingestion, through Storage, Analytics, Discovery, all the way to Consumption. For proving security to data pipeline, few steps are there that are:-

Authentication will verify user’s identity and ensure they are who they say they are. Using the Kerberos protocol provides a reliable mechanism for authentication.

It is the next step to secure data, by defining which dataset can be consulted by the users or services. Access control will restrict users and services to access only that data which they have permission for; they will access all the data.

Encryption and data masking are required to ensure secure access to sensitive data. Sensitive data in the cluster should be secured at rest as well as in motion. We need to use proper Data Protection techniques which will protect data in the cluster from unauthorized visibility.

Another aspect of data security requirement is Auditing data access by users. It can detect the log on & access attempts as well as the administrative changes.

Real-Time Data Monitoring

Data In enterprise systems is like food – it has to be kept fresh. Also, it needs nourishment. Otherwise, it goes wrong and doesn’t help you in making strategic and operational decisions. Just as consuming spoiled food could make you sick, using “spoiled” data may be bad for your organization’s health.

There may be plenty of data, but it has to be reliable and consumable to be valuable. While most of the focus in enterprises is often about how to store and analyze large amounts of data, it is also essential to keep this data fresh and flavorful.

So we can do this? The solution is for monitoring, auditing, testing, managing, and controlling the data. Continuous monitoring of data is an important part of the governance mechanisms. 

Apache Flume is useful for processing log data. Apache Storm is desirable for operations monitoring Apache Spark for streaming data, graph processing, and machine learning. Monitoring can happen in data storage layer. It includes following steps for data monitoring:-

These are the techniques to identify the quality of data and the lifecycle of the data through various phases. In these systems, it is important to capture the metadata at every layer of the stack so it can be used for verification and profiling.Talend, Hive, Pig.

Data is considered to be of high quality if it meets business needs and it satisfies the intended use so that it's helpful in making business decisions successfully. So, understanding the dimension of greatest interest and implementing methods to achieve it is important.

It means implementing various solutions to correct the incorrect or corrupt data.

Policies have to be in place to make sure the loopholes for data loss are taken care of. Identification of such data loss needs careful monitoring and quality assessment processes.

How Can Don Help You?

Don Big Data Solutions can help you at every layer of Big Data Architecture. Don Big Data Services enables enterprises to Build, Manage and deploy Big Data On-Premises, in the Cloud or on Hybrid Cloud Solutions Using Amazon Big Data Solutions, Azure Big Data Solutions and Google Big Data Solutions. Don Big-Data-as-a-Service delivers -

Big Data Infrastructure Solutions

Deploy, Manage, Monitor Big Data Infrastructure on Apache Hadoop and Apache Spark with different storage solutions HDFS, GlusterFS, and Tachyon On-Premises, Hybrid and Public Cloud.

Apache Hadoop & Spark Consulting Services

Don Delivers expert Apache Hadoop and Spark Consulting and Hadoop Support Services. Don offers innovative solutions for Apache Hadoop and Spark and all of its components including - Kafka, Hive, Pig, MapReduce, Spark, HDFS, HBase and more.

Big Data Security Solutions

Big Data Security solution provides authentication, authorization and Audit to Enable Central Security Administration of Apache Hadoop, Apache Spark, HDFS, Hive, Hbase with Apache Knox and Apache Ranger. Secure Mode Cluster deployment of Apache Hadoop and Apache Spark using Kerboses.