Big Data Architecture

The Architecture of Big Data

In this article, I am going to discuss the Architecture of Big Data. Please read our previous article, where we discussed the 5 V’s of Big Data in detail. At the end of this article, you will understand everything about Big Data Architecture.

Big Data Architecture

The intake, processing, and analysis of data that is too huge or complicated for typical database systems is handled by a big data architecture. Sorts of work that are handled by big data architecture: learn more about big data by taking a big data online course.

  • Big data sources are processed in batches.
  • Big data processing in real-time.
  • Machine learning and predictive analytics.
  • A well-designed big data architecture may help your firm save money and predict future trends, allowing you to make better business decisions.

The following components are found in most big data architectures:

Data sources:

The foundation of each big data solution is one or more data sources. Example: Relational databases, for example, are application data storage. Application-generated static files, such as web server log files. IoT devices, for example, are real-time data sources.

Data storage:

Batch processing data is often kept in a distributed file store that can handle massive volumes of large files in a variety of formats. A data lake is a term used to describe this type of storage.

Batch Processing:

Because the data sets are so huge, a big data solution frequently has to filter, aggregate, and otherwise prepare the data for analysis using long-running batch tasks. These jobs usually entail reading source files, processing them, and writing the results to new files.

Real-Time Message Ingestion:

If real-time sources are used, the architecture must allow for the capture and storage of real-time messages for stream processing. This may be a simple data storage, with incoming messages being deposited in a folder for processing.

Stream Processing:

The solution must process real-time communications after capturing them by filtering, aggregating, and otherwise preparing the data for analysis. After that, the data from the processed stream is written to an output sink.

Analytics Data Store:

Many big data solutions prepare data for analysis before serving it in a structured format that can be queried using analytical software.

Analysis and Reporting:

The majority of big data solutions aim to give data insights through analysis and reporting. Interactive data exploration by data scientists or data analysts can also be used for analysis and reporting.

Orchestration:

The majority of big data solutions are made up of processes that change source data, transport data across numerous sources and sinks, load the processed data into an analytical data store, or send the results directly to a report or dashboard. You can utilize orchestration technology to automate these processes.

Big Data Architecture Challenges –

A big data architecture, when done correctly, can save your firm money and help you foresee critical trends, but it’s not without its drawbacks. When working with large data, be aware of the following issues.

Data quality is an issue whenever you’re working with a variety of data sources.

  1. This means you’ll have to put in some effort to make sure the data formats are consistent and that you don’t have any duplicate or missing data that would make your research incorrect.
  2. Before you can combine your data with other data for analysis, you’ll need to examine and prepare it.

Scaling – The volume of big data is what makes it valuable.

  1. You can soon run into problems if your architecture was not built to scale up.
  2. First, if you don’t budget for infrastructure maintenance, the expenditures can quickly add up. This can put a strain on your finances.
  3. Second, if you don’t plan for scale, your results may suffer dramatically. Both of these concerns should be addressed throughout the design stages of your big data architecture development.

Security – while big data can provide valuable insights into your data, protecting that data can be difficult.

  1. Fraudsters and hackers may be interested in your data and attempt to add their own phony data or skim it for crucial information.
  2. A hacker can infiltrate your system and saturate the data with noise, making it impossible to detect criminal activities.
  3. In contrast, your big data contains a large amount of sensitive information, which a cybercriminal may mine for if you don’t secure your perimeters and encrypt your data.

In the next article, I am going to discuss Big Data Technologies. Here, in this article, I try to explain the Architecture of Big Data and I hope you enjoy this Big Data Architecture article.

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