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What is Big Data Architecture? Definition, Layers, Process & Best Practices

Big data analytics has taken centre stage in today’s world. While the overwhelmingly large volume of structured and unstructured data swamps the business world, it is undeniable how this massive amount of data and its analysis has helped businesses make better, more insightful decisions. After all, it is not the volume that matters but what is made of the data.

That brings us to another very crucial aspect of big data, which is big data architecture. The foundation for big data analytics, big data architecture encompasses the underlying system that facilitates the processing and analysis of big data that is too complex for conventional database systems to handle. 

Here is an in-depth guide for you to discover the many aspects of big data architecture and what you can do to specialise in the field of big data.

What is Big Data Architecture?

Big data architecture is the cardinal system supporting big data analytics. The bedrock of big data analytics, big data architecture is the layout that allows data to be optimally ingested, processed, and analysed. In other words, big data architecture is the linchpin that drives data analytics and provides a means by which big data analytics tools can extract vital information from otherwise obscure data and drive meaningful and strategic business decisions. 

Here is a brief overview of some of the most common components of big data architecture:

  • Data sources: The obvious starting point of all big data solutions data sources may be static files produced by applications (web server log files), application data sources (relational databases), or real-time data sources (IoT devices).
  • Data storage: Often referred to as a data lake, a distributed file store holds bulks of large files in different formats, which are subsequently used for batch processing operations.
  • Batch processing: In order to make large datasets analysis-ready, batch processing carries out the filtering, aggregation, and preparation of the data files through long-running batch jobs.
  • Message ingestion: This component of the big data architecture includes a way to capture and store messages from real-time sources for stream processing. 
  • Stream processing: Another preparatory step before data analytics, stream processing filters and aggregates the data after capturing real-time messages.
  • Analytical data store: After preparing the data for analytics, most big data solutions serve the processed data in a structured format for further querying using analytical tools. The analytical data store that serves these queries can either be a Kimball-style relational data warehouse or a low-latency NoSQL technology.
  • Analysis and reporting: One of the critical goals of most big data solutions, data analysis and reporting provides insights into the data. For this purpose, the big data architecture may have a data modelling layer, support self-service BI, or even incorporate interactive data exploration. 
  • Orchestration: An orchestration technology can automate the workflows involved in repeated data processing operations, such as transforming the data source, moving data between sources and sinks, loading the processed data into an analytical data store, and final reporting.

Big Data Architecture Layers

The components of big data analytics architecture primarily consist of four logical layers performing four key processes. The layers are merely logical and provide a means to organise the components of the architecture.

  • Big data sources layer: The data available for analysis will vary in origin and format; the format may be structured, unstructured, or semi-structured, the speed of data arrival and delivery will vary according to the source, the data collection mode may be direct or through data providers, in batch mode or in real-time, and the location of the data source may be external or within the organisation. 
  • Data massaging and storage layer: This layer acquires data from the data sources, converts it, and stores it in a format that is compatible with data analytics tools. Governance policies and compliance regulations primarily decide the suitable storage format for different types of data.
  • Analysis layer: It extracts the data from the data massaging and storage layer (or directly from the data source) to derive insights from the data.
  • Consumption layer: This layer receives the output provided by the analysis layer and presents them to the relevant output layer. The consumers of the output may be business processes, humans, visualisation applications, or services.

Big Data Architecture Processes

In addition to the four logical layers, four cross-layer processes operate in the big data environment. 

  • Data source connection: Fast and efficient data ingression demands seamless connectivity to different storage systems, protocols, and networks, achieved by connectors and adapters.
  • Big data governance: Data governance operates right from data ingestion and continues through data processing, analysis, storage, archive or deletion, and includes provisions for security and privacy.
  • Management of systems: Modern big data architecture comprises highly scalable and large-scale distributed clusters; these systems must be closely monitored through central management consoles.
  • Quality of service (QoS): QoS is a framework that offers support for defining the data quality, frequencies and sizes of ingestion, compliance policies, as well as data filtering.

Big Data Architecture Best Practices

Big data architecture best practices refer to a set of principles of modern data architecture that help in developing a service-oriented approach while at the same time addressing business needs in a fast-paced data-driven world.

  • Align the big data project with the business vision

The big data project should be in line with the business goals and the organisational context with a clear understanding of the data architecture work requirements, frameworks and principles to be used, the key drivers of the organisation, business technology elements currently in use, business strategies and organisational models, governance and legal frameworks, and pre-existing and current architecture frameworks. 

  • Identify and categorise data sources

For data to be normalised into a standard format, data sources must be identified and categorised. The categorisation may either be structured data or unstructured data; while the former is usually formatted through predefined database techniques, the latter does not follow a consistent and well-defined format.

  • Consolidate data into a single Master Data Management system

Batch processing and stream processing are two methods via which data can be consolidated for querying on demand. In this regard, it is imperative to mention that Hadoop is a popular, open-source batch processing framework for storing, processing, and analysing vast volumes of data. The Hadoop architecture in big data analytics consists of four components – MapReduce, HDFS (HDFS architecture in big data analytics follows the master-slave model for reliable and scalable data storage), YARN, and Hadoop Common. In addition, for querying, a relational DBMS or NoSQL database can be used for storing the Master Data Management System.

  • Provide a user interface that eases data consumption

An intuitive and customisable user interface of the big data application architecture will make it easier for the users to consume data. For example, it could be an SQL interface for data analysts, an OLAP interface for business intelligence, the R language for data scientists, or a real-time API for targeting systems. 

  • Ensure security and control

Instead of enforcing data policies and access controls on downstream data stores and applications, it is done directly on the raw data. This unified approach to data security has been further necessitated by the growth of platforms such as Hadoop, Google BigQuery, Amazon Redshift, and Snowflake and made into a reality by data security projects like Apache Sentry. 

How to Build The Big Data Architecture?

Without the right tools and processes in place, big data analysts will spend more time organising data than delivering meaningful analyses and reporting their findings. Hence, the key is to develop a big data architecture that is logical and has a streamlined setup.

Following is the general procedure for designing a big data architecture:

  1. Determining if the business has a big data problem by considering data variety, data velocity, and current challenges. 
  2. Selecting a vendor for managing the big data end-to-end architecture; when it comes to tools for this purpose, the Hadoop architecture in big data analytics is quite in demand. Microsoft, AWS, MapR, Hortonworks, Cloudera, and BigInsights are popular vendors for Hadoop distribution.
  3. Choosing a deployment strategy that may be on-premises, cloud-based, or a mix of both. 
  4. Planning the hardware and infrastructure sizing by considering daily data ingestion volume, multi-data centre deployment, data retention period, data volume for one-time historical load, and the time for which the cluster is sized.
  5. As a follow up to capacity planning, the next step involves infrastructure sizing to determine the type of hardware and the number of clusters or environments needed. 
  6. Last but not least, a backup and disaster recovery plan should be in place with due consideration to how critical is the stored data, the Recovery Time Objective and Recovery Point Objective requirements, multi-data centre deployment, backup interval, and the type of disaster recovery (Active-Active or Active-Passive) that is most apt.

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Conclusion

The unprecedented growth of big data, Artificial Intelligence, and Machine Learning call for effective ways to analyse the massive amounts of data generated every day. Not just that, the reports of the analysis must be able to offer actionable takeaways to steer strategic decision making in businesses.  A solid and well-integrated big data architecture plan not only makes the analysis possible but also brings with it a number of benefits, both in terms of time saved and the insights generated and acted upon.

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