Table of contents:
- Introduction
- What is Big Data?
- What is Big Data Architecture?
- Types of Big Data Architecture
- Big Data Tools and Techniques
- Big Data Architecture Applications
- Conclusion
Introduction
The real power of big data lies in ingesting massive amounts of data from various sources. Big Data Architecture helps manage this enormous amount of data by providing detailed solutions to store, process, and analyze it.
The collected data from different sources can be classified into: structured, semi-structured, and unstructured. A significant Data Architecture manages these classified data types layer by layer and helps in providing data storage, analysis, and reporting.
What is Big Data?
We have access to the internet and smartphone these days, but have you ever wondered how much data a single smartphone can generate? Leave alone a smartphone; think about big organizations and companies. Where do they store this massive amount of data, and how do they manage it all? It’s hard to process all this data for a traditional computer system. So what data do you classify as big data, and how? This is possible with five concepts: quantity, velocity, diversity, integrity, and value.
Let’s understand this concept better with the help of an example: All the data generated by healthcare centers are simultaneously produced every day, which attributes velocity to the data; further, it can be used for other purposes like fast disease detection, better treatments and hugely benefits the health sector which adds integrity and value to the data. Now that we have defined big data let’s discuss how to manage this big data.
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What is Big Data Architecture?
Big Data Architecture is the core system that supports big data analytics. It is a layout where data can be optimally recorded, processed, and analyzed. In other words Big Data Architecture in the backbone of data analytics which helps extract helpful information from otherwise wasted junk files. With the help of data architecture, all the generated data can be utilized and employed in the right direction.
To understand significant data architecture better, let’s understand its layers and components.
There are some of the significant components of data architecture:
1) Data Sources: Identifying and collecting all the data sources and categories is the process. Data that can be collected are usually generated when you use a web application or website, watch videos or use your cellphones. Web server log files, relational databases, or real-time data sources; All sources feeding the data extraction pipeline are subject to this definition.
2) Data Ingestion: It is processed through the data ingestion pipeline after collecting and identifying all the data sources.
3) Data Storage: All the data is stored in the storage area. Data lakes store massive data blocks in various formats for cleansing and transforming data. Once all the data has been collected, classified, and stored, it is sent for data pre-processing.
4) Data Pre-Processing: Before the data is processed, it is pre-processed based on the customer’s or company’s specific criteria or requirements. After cleaning and transforming the data, it is finally sent for data processing. This pre-processing involves a crucial step known as the data cleansing process, where inconsistencies, errors, and discrepancies in the raw data are identified and corrected. The data cleansing process not only eliminates these issues but also ensures the quality and accuracy of the data, making it suitable for further processing.
5) Data Processing: Through data processing, all the data is filtered, aggregated, and prepared for the data analysis of large data chunks. Multiple approaches are used for batch processing, including Hive jobs, U-SQL jobs, Sqoop, or Pig.
6) Real-Time message data ingestion: All the generated data is then sent to a real-time streaming system, ensuring that the generated data is received sequentially and uniformly for the batching process.
7) Streaming Process: All the real-time generated data is sorted and aggregated before data analysis.
8) Analytical data storage: Analytical storage tools are used to prepare the data for further analysis; the tools can be based on HBase or any other NoSQL data warehouse technology.
9) Reporting and Analysis: It generates insights into the processed data and uses interactive visuals to represent the data insights better. To this end, big data architectures can also include a data modeling layer, support self-service BI, and include interactive data exploration.
10) Orchestration: It automates the workflow associated with redundant data processing operations.
Types of Big Data Architecture
There are two types of Big Data Architecture: Lambda Architecture and Kappa Architecture.
Some layers remain constant for the mentioned types: data source, data storage, big data governance, and data consumption.
1) Lambda Architecture
Lambda pattern has both batch and real-time processing. It can be considered as a combination of two systems. I will be discussing three lambda architecture patterns here:
● batch-only serving layer: in this pattern, the batch layer ingests data and computes the values, followed by a dedicated serving layer, and then the consumption layer reads from the serving layer. As mentioned earlier, Lambda architecture is a mixture of batch and real-time processing, the real-time processing here is enabled by a speed layer that ingests, computes, and produces output directly utilized by the consumption layer.
● dedicated serving layer: in this pattern, just like the batch layer has a dedicated serving layer, there is a dedicated serving layer for the speed layer.
● Common serving layer: it combines both the serving layer of batch and the serving layer of speed into one standard layer, which feeds the consumption layer.
2) Kappa Architecture
Kappa architecture eliminates the need for a batch layer, only focusing on real-time processing. Here the real-time processing is done using a stream layer which sends the computed value to a dedicated serving layer. This output is then used by the consumption layer, eliminating batch processing.
Big Data Tools and Techniques
Big data tools can be classified into four categories:
- Massively Parallel Processing (MPP)
MPP, or Massively parallel processing, is a processing paradigm in which hundreds and thousands of nodes perform different parts of a computational task in parallel with their input and output devices and memory. They usually perform everyday computing tasks by communicating with each other over high-speed internet connections.
- No-SQL Databases
A No-SQL or a non-relational database is a structured database that contains all the un-structures heterogeneous data in a domain. It converts all the unstructured data of the domain into a structured form because, without structure, data cannot be stored in the database. No-SQL databases are famous for their scalability and versatility.
- Distributed Storage and Processing Tools
As the name suggests, a distributed database is dispersed over an interconnected network of computer systems. Each database receiver has its processing units. Azure, Amazon EMR, and MS SQL are leading data processing and distribution platforms.
- Cloud Computing Tools
Cloud Computing offers the delivery of configurable computing resources through the internet. It is a paid service that is very useful for handling large amounts of data.
Big Data Architecture Applications
Big data use frameworks such as Cassandra, Hadoop, and Spark to store and analyze chunky data. Though there can be many other applications of Big Data Architecture, I have only discussed two.
1) Healthcare
A considerable amount of data is produced every year in health sectors; with growing technologies, it is anticipated that health sectors will grow immensely; thus, utilizing all the present resources in the best possible way will save future expenditures on these sectors. Using significant data architecture in the healthcare field will help them analyze all their resources, providing them with better solutions. Other benefits include detecting or treating diseases at an early stage or analyzing the best possible treatment for their patients.
2) Manufacturing Sector
The manufacturing Sector is the backbone of the economy and has always worked towards finding innovations and technologies to bring more efficiency and improve work quality. To achieve higher sustainability and growth rate, countries have started analyzing their data sets—the manufacturing sector stores more data than any other sector. The source of such massive data production should use significant data architecture to efficiently use all of its data effectively and contribute to the nation’s development. Some other benefits: Help in researching better. New data architecture allows old data sets to collaborate with new data sets and use all the existing information more comprehensively. Help manufacturers improve their products using market extract data sets.
Conclusion
We need to think and analyze to make scientific and technological progress. For human beings, it’s their brain that does this job. Just like that, Big Data Architecture can be seen as a digital brain. Its growth requires effective methods for analyzing the data generated daily. Not only that, but the analytical reports should also be able to provide actionable insights to guide the company’s strategic decisions. A robust and well-integrated big data architecture plan enables analysis and provides many benefits in terms of time-saving and gaining insights.
