Meaning of Data Architecture
Data architecture is the base of an effective data strategy. It is a framework of models, policies, rules, and standards that an organization uses to manage data and its flow. In a company, everyone wants data that is easily accessible, cleaned up well, and updated regularly. Successful data architecture standardization is the procedure for capturing, storing, transforming, and delivering valuable data to people who need it. It identifies the business users who will consume the data and their changing needs.
An excellent approach for data architecture is to make the data flow from data consumers to data sources, not any other way. The aim is to transform business needs into data and system needs. Companies require a centralized architecture that goes along with business procedures and clarifies the whole aspects of data. The single components of data architecture are the outcomes, activities, and behaviors.
It is the apprehension for data architects. A data architect assists in building, optimizing, and maintaining conceptual and logical database models. They regulate how to source data that can push the business forward and how that distribution is done to give valuable insights to decision-makers.
Data Architecture Framework
Various enterprise data architecture frameworks are used to build an organization’s data architecture framework.
DAMA-DMBOK 2
This data architecture framework of DAMA International’s Data Management Body of Knowledge – is a framework that is designed especially for data management. It includes standardized definitions of data management terminology, functions, delivery, and roles and also represents guidelines on data management.
Zachman Framework for Enterprise Architecture
The Enterprise data architecture framework of John Zachman was made at IBM during the 1980s. The ‘data’ column of this data architecture framework involves various layers like critical architectural standardization for the business, a semantic or conceptual data model, an enterprise or logical data model, a physical data model, and actual databases.
The Open Group Architecture Framework (TOGAF)
TOGAF is the most used enterprise data architecture framework, which offers a framework to design, plan, implement, and manage data architecture best practices. It assists define business goals that align them with architectural objectives.
Data Architecture Components
The various data architecture components at present:
Data Pipelines:
These architecture components contain the procedure of data collection, how the refinement is done, storage, Analysis, and the flow of data from one place to another. the whole process from where data is collected and transferred to and how movement of data is done through data pipelines
Cloud storage:
This data architecture component refers to the cloud at an off-site location where the data which was stored is approached through the internet
APIs:
API data architecture ensures the communication between the host and through an IP address where communication occurs. Different types of information are communicated to the user by the API like
AI & ML models:
AI and ML data architecture components administer an automated system for the data. Calculating the decisions that can be made and predicted through them is done along with data collection, labeling, etc.
Data streaming refers to the continuous flow of data from a point to a destination, which is processed for real-time Analysis.
Kubernetes:
It is the platform of data architecture components to compute, network, and store the infrastructure workload
Cloud computing refers to the process in which the data can be analyzed, stored, and managed from the cloud. The application of cloud computing gives profits like low cost, secure data, and no need for managing the IT infrastructure as the cloud does the management.
Real-time analytics:
It involves analyzing the real-time data to get an insight into it. Based on this Analysis, the organizations can make their decisions.
What is Big Data Architecture
Big data architecture is a comprehensive solution to deal with massive data. Big data architecture details the blueprint for solutions and infrastructure to deal with big data based on a company’s application. Big data architecture defines the components, layers, and methodology of communication. The reference point is the ingestion, process, storage, management, access, and data analysis.
There are multiple workload types involved in big data architecture systems, and they are mainly classified as follows:
- Merely batching the data where big data-based sources are at rest is a data processing situation.
- Can achieve real-time processing of Big data architecture with motion-based processing.
- The Analysis of new interactive big data technologies and tools.
- The usage of machine learning and predictive Analysis.
Components of Big Data Architecture
Data Sources
the whole sources that feed into the data extraction pipeline are subject to the definition of Big data architecture. This is where the starting point for the big data pipeline is addressed. Data sources, open and third-party, play a crucial role in it. Relational databases, data warehouses, SaaS applications, real-time data from servers of the company and sensors like IoT devices, and static files such as Windows logs have various data sources. Both batch processing and real-time processing are possible. The data managed can be both batch processing and real-time processing.
Data Storage
in this Big data architecture, data is stored in file storage that is dispersed in nature and can hold various format-based big files. It can store many different format-based big files in the data lake. Data storage includes the data managed for batch-building operations and saved in the file stores. We provide HDFS, Microsoft Azure, AWS, GCP storage, and other blob containers.
Batch Processing
Each piece of data is split into multiple categories using long-running jobs to filter, aggregate, and prepare data for Analysis. These jobs need sources, process them, and deliver the processed files to new files. Various approaches to batch processing are employed, involving Hive jobs, U-SQL jobs, Sqoop and customized map reducer jobs written in any Java or Scala or other programming languages such as Python.
Real Time-Based Message Ingestion
A real-time streaming system that helps the data being generated in a sequence and uniform fashion is a Big data architecture. Compared to batch processing, this involves all real-time streaming systems that help the data to generate at the time it receives. This data mart or store, which gets all incoming messages and discards them into a folder for data processing, is the only one that needs to be contacted. Message-based ingestion stores such as Apache Kafka, Flume, Event hubs f, and others, on the other hand, should be used if message-based processing is needed. The delivery process, with other message queuing semantics, is most of the time more reliable.
Stream Processing
Real-time messages for the ingestion and streaming procedure are different. The latter uses the ingested data as a publish-subscribe tool, whereas the former takes into account all of the ingested data in the first place and then utilizes the publish-subscribe tool. On the other hand, stream processing handles all that streaming data in the form of windows or streams and writes it to the sink. Stream processing includes Apache Spark, Flink, Storm, etc.
Analytics-Based Datastore
Analytical tools in Big Data Architecture use the data store based on HBase or any other NoSQL data warehouse technology for analyzing and processing already processed data. It can represent the data with the assistance of a hive database, which can give metadata abstraction, or interact user of a hive database, which can give metadata abstraction in the data store. NoSQL databases like HBase or Spark SQL are also available.
Reporting and Analysis
this Big data architecture is generated insights, on the other hand, for the process, and that is the effective accomplishment of the report and analysis tools for the utilization of embedded technology and a solution for the production of valuable graphs, Analysis, and insights that are helpful l to the businesses—for example, Cognos, Hyperion, and others.
Orchestration
Big data architecture Data-based solutions that utilize big data are data-related tasks that repeat in nature and contain workflow chains that are transformed through source data and also move data across sources, sinks, and loads in stores. Sqoop, oozie, data factory, and others are examples.
