Big Data Architecture Layers
Big data analytics architecture consists essentially of four logical layers that perform four key tasks. The layers are purely logical and serve as a way to structure the architecture’s components.
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 depending on the source; the data collection mode may be direct or through data providers; the data collection mode may be the batch mode or real-time, and the data source location may be external or within the organization.
Data massaging and storage layer: Data acquisition, conversion, and storage layer: This layer collects data from data sources, converts it, and stores it in a manner that data analytics tools can understand. The appropriate storage format for various types of data is generally determined by governance policies and compliance standards.
Analysis layer: To gain insights from the data, the analysis layer collects data from the data massaging and storage layer (or straight from the data source).
Consumption layer: The consumption layer receives the output from the analysis layer and passes it on to the appropriate output layer. Business processes, individuals, visualization programs, and services could all be consumers of the output.
Big Data Architecture Processes
Big data analytics architecture consists essentially of four logical layers that perform four key tasks. The layers are purely logical and serve as a way to organize the architecture’s components.
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 depending on the source; the data collection mode may be direct or through data providers; the data collection mode may be the batch mode or real-time, and the data source location may be external or within the organization.
Data massaging and storage layer: Data acquisition, conversion, and storage layer: This layer collects data from data sources, converts it, and stores it in a manner that data analytics tools can understand. The appropriate storage format for various types of data is generally determined by governance policies and compliance standards.
Analysis layer: To gain insights from the data, the analysis layer collects data from the data massaging and storage layer (or straight from the data source).
Consumption layer: The consumption layer receives the output from the analysis layer and passes it on to the appropriate output layer. Business processes, individuals, visualization programs, and services could all be consumers of the output.
Big Data Architecture Processes
In the big data environment, four cross-layer processes operate in addition to the four logical layers.
Data source connection: Fast and efficient data ingression necessitates seamless connections to various storage systems, protocols, and networks, which connectors and adapters may provide.
Big data governance: Data governance begins with data ingestion and continues through data processing, analysis, storage, archive, or deletion, with security and privacy protections in place.
System administration: Modern big data architecture consists of large-scale distributed clusters that must be continuously monitored via central management consoles.
Quality of service (QoS): Quality of service (QoS) is a framework that aids in the definition of data quality, ingestion frequency and size, compliance regulations, and data filtering.
Big Data Architecture Best Practices
Big data architecture best practices are a set of modern data architectural concepts that aid in the development of a service-oriented approach while also addressing business objectives in a fast-paced data-driven world.
Align the big data initiative to the company’s goals.
With a clear understanding of the data architecture work requirements, frameworks, and principles to be used, the key drivers of the organization, business technology elements currently in use, business strategies and organizational models, governance and legal frameworks, and pre-existing and current architecture frameworks, the big data project should be in line with the business goals and the organizational context.
Recognize and classify data sources.
Data sources must be identified and categorized before data can be normalized into a common format. Structured data or unstructured data are the two types of categorization; the former is normally formatted using predetermined database techniques, whereas the latter does not have a consistent and well-defined format.
Data should be consolidated into a single Master Data Management system.
Batch processing and stream processing are two ways for consolidating data for on-demand querying. It’s important to note that Hadoop is a prominent open-source batch processing framework for storing, processing, and analyzing large amounts of data in this regard. MapReduce, HDFS (HDFS design in big data analytics follows the master-slave model for dependable and scalable data storage), YARN, and Hadoop Common are the four components of the Hadoop architecture in big data analytics. A relational DBMS or a NoSQL database can also be used to store the Master Data Management System for querying.
Create a user interface that makes it easier to consume data.
The big data application architecture’s intuitive and flexible user interface will make it easier for consumers to ingest data. An SQL interface for data analysts, an OLAP interface for corporate intelligence, the R language for data scientists, or a real-time API for targeting systems are just a few examples.
Ascertain safety and control.
It is done directly on the raw data, rather than enforcing data policies and access controls on downstream data repositories and applications. The rise of systems like Hadoop, Google BigQuery, Amazon Redshift, and Snowflake has demanded this holistic approach to data security, which has been made a reality by data security initiatives like Apache Sentry.
