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Big Data has become a new field of research and application in the last few years. As Data volume, velocity, variety, and value keep growing exponentially; we must ensure we can handle them all effectively. This article will provide you with an overview of big data architecture.

What is Big Data Architecture? 

Big data architecture is the process of designing, developing, and deploying solutions that utilize a wide range of data sources. It's essential to understand how to create a big data architecture to use your data in the most efficient way possible. 

But why is it so important? 

Suppose you're analyzing something that has nothing to do with statistics or machine learning, like how many people buy one specific product at a particular retailer each year. Well, you'll need an entirely different type of architecture than if you were analyzing something like clickstreams worldwide!

Learn more about big data techniques in a data analytics course in Chennai. 

1. Data sources — These are the data sources that your business uses to generate insights and make decisions. For example, if you own a car repair shop, you may access vehicle information such as mileage, when it was purchased or serviced, and its maintenance history. This information would be considered a "source" of information because it's used by the business itself to make important decisions about how people should be treated when they visit your store for repairs.

2. Data stores — These are physical locations where your organization stores its data. Today's most common database organizations use relational databases such as MySQL (MySQL) or PostgreSQL (PostgreSQL). Others include NoSQL databases such as MongoDB (MongoDB), Cassandra (Cassandra), Couchbase (Couchbase), Redis (Redis), etc.

3. Data processing pipelines: This refers specifically to how you use these different types of

There are three major categories of big data architecture:

Distributed Storage 

This type of architecture uses multiple servers to store the data. It provides fault tolerance and high availability because it distributes the load across multiple servers, which can be located in different locations. However, this approach can be costly due to the additional hardware costs involved with establishing other servers.

2. Hierarchical Storage

This type of architecture uses one or more central servers to store the information and then distributes queries through these servers in a round-robin fashion until they reach their destination server. This approach is often used when extensive datasets must be searched through without having access to each individual record (for example, when searching for a specific piece of information within a database).3. Columnar Storage

This type of architecture stores each record as a single flat file rather than using relational databases like MySQL or Oracle's In Big data architecture is a framework for designing an organization's data-centric information architecture. Big data architecture aims to establish a system that can efficiently handle the processing and storage of large amounts of data while also improving the overall business value.

Benefits of big data architecture

High-Performance parallel computing 

Big data architectures use parallel computing, wherein multiprocessor servers conduct several calculations simultaneously to accelerate the process. By parallelizing large data sets on multiprocessor computers, large data sets can be processed quickly. Part of the job can be completed concurrently.

Elastic scalability 

Big Data architectures allow for horizontal scaling, which enables the environment to be adapted to the magnitude of the workloads.

Big data solutions are typically run in the cloud, where you only pay for the processing and storage power you use.

Freedom of Choice

Big Data architectures can use various commercially available platforms and products, including Apache technologies, MongoDB Atlas, and Azure-managed services. You can choose the best combination of solutions for your unique workloads, installed systems, and IT expertise levels to get the best outcome.

The ability to interoperate with other systems 

To build integrated platforms for various workloads, you can leverage Big Data architecture components for IoT processing, BI, and analytics workflows.

Applications of big data architectures

Different big data architecture layers

The four logical layers that perform the four fundamental activities make up most of the big data analytics architecture components. The layers serve only as an analytic organization tool for the architecture's parts.

1. Big Data source layer – The sources and formats of the data that can be analyzed will differ. The format could be structured, unstructured, or semi-structured; the speed of data arrival and delivery will vary depending on the source; the method of data collection may be direct or through data providers; batch mode or real-time; and the location of the data source may be internal or external to the organization.

2. Data massaging and storage layer — This layer gathers information from the data sources, transforms it, and stores it in a format that data analytics programs can use. Governance policies and compliance standards generally determine the most appropriate storage format for various data types.

3. Analysis layer – To gain insights from the data, it collects the data from the data massaging and storage layer (or straight from the data source).

4. Consumption layer – This layer accepts the output from the analysis layer and presents it to the appropriate output layer. The output's consumers could be people, business processes, visualization software, or services.

Applications of big data architectures

Using and implementing big data applications is vital to big data architecture. In particular, the following big data applications are used and applied by the big data architecture:

. Due to its data ingestion process and data lake storage, the big data architecture's structure enables the deletion of sensitive data from the beginning.

A big data architecture involving batch or real-time ingests data in both formats. There is a regular schedule and frequency for batch processing. 

Data from the table is divided using SQL, U-SQL, or Hive queries. By splitting the tables, the query performance is enhanced. Since data files can be segmented, the ingestion process and job scheduling for batch data are more straightforward.

Distributed batch files can be further divided using parallelism and quicker work times. Workload allocation across processing units is also employed.

The static batch files are built and saved in further-splittable file formats. The formats used to create and store the static batch files might be further divided. The Hadoop Distributed File System (HDFS) can process files simultaneously across hundreds of nodes, reducing job times over time. The Hadoop Distributed File System (HDFS) may group hundreds of nodes and process files in parallel, thus reducing job times.

Conclusion

It is important to understand the different components of big data architecture and how each of the components can impact a big data strategy. With a proper understanding of big data architecture, companies can prepare to handle structured and unstructured data. It helps them make strategic decisions about what actions they should take with those data sets.