Big Data Architecture
Writer : Michael Aurora EG
It is easier to design a Data Pipeline using Big Data Architecture when dealing with both batch and stream processing system requirements. There are six layers in this architecture to ensure that data does not get out of hand.
Layers of Big Data Architecture
- Data Ingestion Layer
- Data Collector Layer
- Big Data Processing Layer
- Data Storage Layer
- Data Query Layer
- Big Data Visualization Layer
- Data Security Layer
- Data Monitoring Layer
Big Data Ingestion Layer
This layer of Big Data Architecture serves as the starting point for data that comes from a variety of sources. Data ingestion is the process of prioritizing and categorizing the data so that it can flow more easily through the Data ingestion process layers.
Data Collector Layer
Here, we're focusing more on delivering data from our ingestion layer to the rest of our data pipelines. A layer of data architecture where analytic capabilities can begin is the decoupling layer.
Data Processing Layer
There is a lot of emphasis on data pipeline processing in this first layer of Big Data Architecture We can say this layer processes the data we collected in the previous layer. The analytic may begin here, as we perform some magic with the data in order to route them to a different location and classify the data flow.
Data Storage Layer
When dealing with large amounts of data, storage becomes a major issue. If you run into this problem, you might want to look into Data Ingestion Patterns as a solution. Finding a storage solution is critical when your data grows in size. Big Data Architecture's "where to store such large data efficiently" is the focus of this layer.
Data Query Layer
At this level, Big Data analytics are active. It is important to gather as much data as possible for the next layer.
Data Visualization Layer
In the presentation or visualization tier, where the data pipeline users can feel the VALUE of DATA, visualizations are most prominent. What you need is a way to draw people in, make your findings clear, and keep them interested.
Big Data Ingestion Layer
The Layered Architecture of Big Data helps to shed light on the mystique surrounding the ingest of data. With a layered architecture, each layer in the Big Data ingestion process serves a specific purpose.
a layer for processing large amounts of data (Tools, Use Cases, Features)
Gathering data from various sources and making it available for further processing is what this layer of Big Data Architecture is all about.
Now that the data has been prepared, all that is left for us to do is route it to its various destinations.
The primary goal of this layer is to specialize the Data Pipeline processing system, or, to put it another way, to process the data collected in the previous layer.
This system is a simple batch processing system for offline data analysis. Apache Sqoop is the tool of choice for this task.
What is Apache Sqoop?
Using this tool, large amounts of data can be moved quickly and easily between relational databases and Apache Hadoop. Using Apache Sqoop, you can export Hadoop data into structured data stores outside of your organization.
Oracle, MySQL, PostgreSQL, and HSQLDB are some of the relational databases supported by Apache Sqoop.
Functions of Apache Sqoop
- Import sequential data sets from the mainframe
- Data imports
- Parallel Data Transfer
- Fast data copies
- Efficient data analysis
- Load balancing
Processor Capable of Near-Real-Time Analysis
An all-in-one system for online analytics processing. It's best to use Storm for this kind of processing. the warning system receives notifications from Apache Storm, which decides how urgent the situation is (dashboard, e-mail, other monitoring systems).
What is Apache Storm?
When data is fed into the system, it is processed in real time. Data can be processed in real-time, and Enterprise Hadoop now has more reliable real-time processing capabilities. Storm on YARN is a powerful tool for scenarios that call for real-time analytics, machine learning, and on-going monitoring of operations in progress.
Apache Storm Has the Following 6 Important Features:
- Fast: Each node can handle one million 100-byte messages per second.
- There are no limits to how many computers it can handle at the same time.
- Storm is fault-tolerant, which means that if a worker dies, Storm will restart them. The worker will be restarted on a new node if a node is lost.
- Storm guarantees that every data unit (tuple) will be processed at least once or exactly once. In the event of a failure, messages are only replayed.
- It has standard configurations that are ready for production right out of the box, making it easy to get up and running. Storm's data ingestion is straightforward once it's installed.
- Batch and Real-time processing system capabilities are combined in a Hybrid Processing System. Apache Spark and Apache Flink are two such processing tools.
What is Apache Spark?
A fast, in-memory data processing engine, Apache Spark Optimization allows data workers to efficiently execute streaming, machine learning, or SQL workloads that need fast iterative access to data sets.
Developers all over the world can now take advantage of Spark's power, gain new insights, and improve their data science workloads by running it on Apache Hadoop YARN.
What is Apache Flink?
Open source Apache Flink provides accurate results even if the data is out-of-order or late-arriving or Distributed Data Processing Apache Flink in the Data Ingestion Pipeline. The following are some of its unique characteristics:
Apache Flink's most important features
- Has high throughput, low latency, and a robust data ingestion framework for large-scale data ingestion on thousands of machines.
- APIs and domain-specific libraries for Batch and Streaming Data Flows, as well as Machine Learning and Graph Processing
Apache Flink Use Cases
- Ecommerce search results are optimized in real time.
- An online stream processing service for data scientists and analysts
- ETL for Business Intelligence Infrastructure that monitors and detects network and sensor errors.
Big Data Storage Layer
Keeping data in the correct location according to usage is a major concern for any data ingestion process. Over the years, our relational databases have proven to be an effective means of storing our data.
When it comes to data ingestion, you should no longer assume that your persistence should be relational because of the new strategic enterprise applications of Big data analytics.
Because we have so many different types of data, we need a variety of different databases to handle it all. Because of this, the Polyglot Persistence concept has been introduced in the database world.
It is the concept of using multiple databases to power one application. In order to share or divide your data among multiple databases, you can use polyglot persistence. It makes use of the various databases' strengths. There are a variety of ways to look at the data in this section. In a nutshell, this means selecting the best tool for the job.
Data ingestion applications should be written in a variety of languages to take full advantage of the fact that different languages are better suited to dealing with specific problems depending on the Data ingestion framework being used.
Polyglot Persistence has many advantages.
- The response times of your app will be much faster because we've combined all the database features into a single app.
- Your app is exceptionally good at handling data because of this. When you model your databases correctly for the data you intend to store, all of the NoSQL databases will scale well.
- When you combine the power of multiple databases, you get a truly immersive experience. MongoDB does not have the ability to return results based on relevance, such as in an e-commerce app where you are searching for products.
Big Data Storage Tools
The following are some of the different types of Big Data storage tools:
HDFS: Hadoop Distributed File System
- HDFS is a Java file system that enables large clusters of commodity servers to provide scalable and reliable data storage.
- It has a lot of storage space and is easy to use.
- There are multiple machines used to store the data because it is so large. In the event of a system failure, the redundant storage of these files could save the system's data.
- In addition, HDFS provides applications for parallel processing of data ingestion. Individual files in HDFS can be as large as terabytes in size, making it ideal for applications that deal with large data sets.
- For each cluster, there is a NameNode that manages file system operations and a supporting DataNode that manages data storage on each compute node.
- Data can be broken down into smaller pieces and distributed to different nodes in a cluster, allowing for parallel processing, when HDFS receives data from a user
- Data ingestion's file system duplicates every bit of data it receives. One copy is placed on each server rack as it is distributed to the various nodes.
- Apache Hadoop's data ingestion framework is built around HDFS and YARN, which are both part of the data management layer.
Features of HDFS
- Distributed storage and processing are both possible with this technology..
- In order to communicate with HDFS, Hadoop provides a command line interface (CLI).
- Cluster status can be checked quickly thanks to built-in servers on the name node and data node.
- Access to file system data in the Data Ingestion Process Flow.
- In order to protect files, HDFS provides access control and authentication mechanisms.
Distributed file system GlusterFS
If a storage solution is to be effective, it must be able to expand and contract without affecting current operations. Unstructured data, such as documents, images, audio and video files, and log files, is well-suited to scale-out storage systems based on GlusterFS. GlusterFS is a network filesystem that can be scaled up and down as needed. Streaming media, data analysis, data ingestion, and other data- and bandwidth-intensive tasks can all benefit from large, distributed storage solutions built on top of this technology.
- It's free and open source software.
- Commodity hardware servers can be used to deploy GlusterFS.
- Performance and storage capacity scale linearly.
- Store data in storage that can be accessed by thousands of servers and scaled up to several petabytes in size.
GlusterFS Use Cases
- Cloud Computing
- Streaming Media
- Content Delivery
Amazon S3 Storage Service
- Simple web service interface for object storage (Amazon S3) allows you to store and retrieve any data from anywhere on the web.
- In terms of durability, it's rated at 99.9% and can handle billions of objects at once. For cloud-native applications, S3 serves as the primary storage, as a "data lake" for analytics, and as a backup and recovery and disaster recovery target for S3-enabled services. Big data processing is now possible thanks to a new, serverless architecture.
- You can easily migrate large amounts of S3 data into or out of Amazon's cloud storage options.
- With Amazon S3, it's easy to archive data in classes like S3 Standard — Infrequent Access and Amazon Glacier, which are both cheaper and more long-term.
Big Data Query Layer
Analytic processing occurs in this layer of data architecture. This is an area traditionally dominated by SQL expert developers because interactive queries are required. It takes a long time to analyze data before Hadoop because of the lack of storage.
In order to store new data, it must first go through an extensive ETL process before the data can be stored in a database or data warehouse. Two critical steps in creating a Data ingestion framework were the data ingestion and data analytics processes, which solved problems with so much data.
Big data analytics is used by businesses of all kinds to –
- Increase revenue
- Decrease costs
- Increase productivity
Query Tools for Big Data Analysis
Apache Hive Architecture
- Apache Hive is a Hadoop-based data warehouse for summarizing, ad-hoc querying, and analyzing massive datasets.
- A Hive query, summarization, exploration, and analysis can then be used to generate actionable business intelligence.
- HiveQL is a SQL-like language that can be used to query data stored in Hadoop's distributed file system.
Features of Apache Hive
- In order to query data, use a SQL-based programming language.
- Even in the face of massive datasets, interactive response times.
- Commodity machines can be added without reducing performance as data volume and variety increase. Utilizes well-established data integration and analytic software.
Apache Spark SQL
To speed up queries, Spark SQL includes a cost-based optimizer, columnar storage, and code generation.
This also scales to thousands of nodes and long-running queries thanks to the Spark engine, which has full fault tolerance in the middle of a query.
Using Spark SQL, you can perform a variety of structured data analysis tasks. Spark SQL performs the following tasks:
- Spark SQL's interfaces give it access to more details about the data and computation structure.
- Spark SQL makes use of this additional data internally to improve performance even further.
- Spark SQL can be used to run SQL queries.
- Spark SQL can be used to read data from an existing Hive server.
It is a cloud-based, petabyte-scale data warehouse service provided by Amazon Redshift. The data is loaded and queried using Amazon Redshift. Running a SQL command can create additional databases if necessary. Most importantly, it's capable of handling data sets as large as a petabyte or even larger.
Data ingestion can be used to gain new insights for your company and customers. A data warehouse can be set up, operated, and scaled using the Amazon Redshift service.
Creating a Data Ingestion framework includes provisioning capacity, monitoring, and backing the cluster, and applying patches and upgrades to the Amazon Redshift engine.
For Big Data, Presto is a SQL query engine
Data sources ranging from gigabytes to petabytes can be queried using Presto, an open-source distributed SQL query engine.
It was built to take advantage of the speed of commercial data warehouses while being scalable enough for Facebook and other large organizations.
- In addition to Hive, Cassandra and relational database systems, Presto can query data stored in proprietary data stores.
- When you run a single Presto query, you can pull data from multiple sources and analyze it across the board.
- In the data ingestion process flow, Presto is designed for analysts who expect response times of less than a second to a minute.
- Presto eliminates the false choice between a fast commercial solution or a slow "free" solution that necessitates a lot of hardware.
Who Uses Presto?
In order to perform interactive queries on the company's 300PB Data Warehouse, Facebook makes use of Presto. Every day, more than 30,000 queries are run on a petabyte of data by more than 1,000 Facebook employees using Presto. Presto is being used by a number of high-profile internet companies, including Airbnb and Dropbox.
Layer for Visualization of Massive Data (Tools, Features)
Measures the project's success are made by this layer of Big Data Architecture. This is how the user views the value of the data. Hadoop and other tools, while useful for handling and storing large volumes of data, lack built-in provisions for data visualization and information distribution, leaving no way to make that data easily accessible to end business users in the Data ingestion pipeline.
Tools for Creating Dashboards with Data Visualization
The following is a list of various tools that can be used to create Data Visualization dashboards:
Created Dashboards for Data Analysis
Unique data visualizations can be achieved through the use of custom dashboards. As an illustration, you could:
- A single custom dashboard can display all of the information about the web and mobile applications, the server, custom metrics, and plugin metrics.
- Create grid-based dashboards that display graphs and tables of the same size and shape.
- You can choose from a variety of pre-built New Relic dashboards, or you can create your own.
Dashboards for Visualization in Real Time
Dashboards can be used to save, share, and communicate information in real time. By revealing the breadth, depth, and variety of their data stores, it aids users in the generation of new questions.
- As new data is collected, dashboards are constantly updated.
- Zoomdata allows you to create a data analytics dashboard with just one chart, and then add to it as necessary.
- Dashboards can display multiple visualizations from multiple connections side-by-side, if desired.
- Dashboards can be quickly built, edited, filtered, and deleted, and then shared or integrated into your web application. You can also move and resize them.
- It is possible to save a dashboard's layout as an image or as a JSON file.
- You can also talk to Data Visualization Experts or make multiple copies of your dashboard in the data ingestion process flow.
Data Visualization with Tableau
Tableau is the most comprehensive data visualization tool on the market, with Drag and Drop capabilities.
- It is possible to create Charts, maps, tabular, matrix reports, stories, and dashboards with Tableau without any technical knowledge.
- There's no limit to what anyone can do with it. Whatever the size, complexity, or number of rows in your graph database, you can turn Big Data Analytics into big ideas using Graph Databases.
- During data ingestion, it can perform quickly in-memory by directly connecting to local and cloud data sources or importing data.
- Easy-to-understand visuals and interactive web dashboards help you make sense of big data.
Apache Kafka Security with Kerberos on Kubernetes may also be of interest.
Using Kibana to Investigate Massive Data Sets
- In the dashboard of Kibana, you can see a collection of saved views. In order to meet your needs, you can arrange and resize the visualizations and save dashboards, which can be reloaded and shared.
- Your data ingestion framework will benefit from Kibana's analytics and visualization platform built on Elasticsearch.
- One of the most important aspects of a project's implementation is the use of Application Performance Monitoring (APM). APM solutions give development and operations teams near real-time insight into how applications and services perform in production, allowing for proactive tuning of services and early detection of possible production issues.
- It allows you to pick and choose how you want to present your data. Data ingestion using Parallel Processing Applications doesn't always require you to know what you're looking for.
- Classics like histograms, line graphs, pie charts, sunburts, and more are included in Kibana core. For data ingestion, they use Elasticsearch's full aggregation capabilities.
The Kibana user interface has four sections:
Intelligence Agents: A Basic Introduction
When a computer program acts in the person's best interests, we say that program is an intelligent agent. In order for intelligent agents to be effective, people must be able to delegate tasks to the software. Agents are capable of automating routine tasks, recalling items you've forgotten, summarizing complex data intelligently, gaining knowledge from their interactions with you, and even making recommendations.
Searching through corporate data or surfing the Internet without knowing where to find the right information can be made easier by using an intelligent agent to assist you. It could also save you from having to deal with all the new information that was constantly being added to the Internet. In addition, an agent may be able to detect changes in its surroundings and respond accordingly.
In the Data ingestion pipeline, an agent can run on a server, but it can also run on a user's system if the user has left the system.
- Recommender systems learn about a user's preferences by analyzing the user's interactions with the system. When developing recommendations, recommender systems must first build an accurate model of the user.
- It is necessary to represent the data in a user model in such a way that the data can be matched to items in the collection.
- During data ingestion, what kinds of data can be used to build a user profile? A user's previous experiences are obviously important. It is possible to use other information, such as the item content or user perceptions of the item, as well.
- Most recommender systems focus on information filtering, which is the delivery of elements selected from a large collection that are likely to be interesting or useful to the user (i.e., relevant).
- Information filtering systems that recommend items to users are known as recommender systems. Mass customization is a marketing strategy employed by some of the most popular e-commerce sites.
- Information retrieval systems and content-based filtering systems often employ similar methods (such as a search engine). Items in the domain must be described in some way in both systems. An information retrieval system does not need to model a user's preferences for an extended period of time in order to use a recommender system.
- In order to improve recommendation systems, there are a number of different techniques for ingesting data.
Useful UI components that display data that changes over time are encouraged.
Useful Features of React
- Components are the heart and soul of React. Everything should be viewed as a component. When working on larger projects, this will make it easier for you to keep the code clean.
- Making it easy to understand your app, React implements one-way data flow, making it simple to think about it. It's important to keep your data in a single direction by using the pattern of flux.
Security and Data Flow Layers for Big Data
As with any type of information, safety is a top priority, and this holds true for the architecture of Big Data as well. It's the most important part of any project. Starting with Ingestion, work your way up through Storage, Analytics, Discovery and finally Consumption. There are a few steps that can be taken to ensure the security of data ingestion to the data pipeline:
Big Data Authentication
Verifying the user's identity and ensuring that they are who they claim to be is the purpose of authentication. Kerberos provides a secure way to authenticate a user account.
Secure information is best protected by defining which datasets can be accessed by users or services. Users and services will be able to access all of the data in the Data ingestion framework because of access control, which restricts them to only using data for which they have permission.
Encryption and Data Masking
Secure access to sensitive information necessitates the use of encryption and data masking. Protecting information while it is in transit and at rest should be a priority for the cluster.
Auditing Data Access by users
Additional data security requirements include auditing data access in the Data ingestion pipeline. The log and access attempts, as well as administrative changes, can be detected by this tool.
Data Monitoring Layer
In enterprise systems, data is like food—you need it to be fresh.. In addition, it requires nourishment to thrive. Unless you do, it will not assist you in making strategic or operational decisions and will go wrong. Using "spoiled" data may have the same effect on your organization's health as eating spoiled food does.
Even if there's a ton of data flowing through the Data Ingestion process, it must be of value. While storing and analyzing large amounts of data is often the main focus in businesses, it is also essential to keep this data fresh and flavorful.
Is this possible?
Monitoring, auditing, testing and managing the data is the solution. As part of the governance mechanisms, constant monitoring of data is essential
Log data can be processed using Apache Flume. Apache Storm is a good choice for monitoring operations. Streaming data, graph processing, and machine learning are all supported by Apache Spark. The data storage layer is one place where monitoring can take place. The following are the steps involved in keeping track of the data:-
Data Profiling and lineage
These are the methods used to track the progression of a piece of data from one stage of its lifecycle to the next. For verification and profiling in these systems, it is critical to capture metadata at every layer of the stack. Talend, Hive, and Pig are all examples of open source software.
High-quality data has been ingested. If it's useful to the company, then it's being used for its intended purpose of helping the company make good business decisions. As a result, it's critical to identify the most important aspect and then put strategies in place to make progress there.
There are many ways to fix inaccurate or corrupt data.
Data Loss and Prevention
In order to prevent data loss, policies must be put into place. Monitoring and quality assessment processes in the Data ingestion process flow are necessary to identify such data loss.
Large amounts of data that are too complex or large for traditional database systems can be processed, ingested, and analyzed with the help of big data architecture. Large amounts of data must be managed and steered in order for business analytics to be effective, and a big data architecture framework serves as a reference blueprint for big data infrastructures and solutions. Big data analytics tools rely on this framework to extract critical business information.
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