Data is now an invaluable resource in today’s cutting-edge markets. With data, you can answer all queries related to your business, customers, or the market. It has become essential to properly store and manage your data to achieve the best results.
If care is not taken then a lot of things can go wrong with your data- some of your data sources may be unreliable, the data might get modified unintentionally when a large number of people use the same means to get data, storage applications may not be able to cope up with the growing data, etc.
Everything might get out of your control if you don’t plan your data management properly with the help of Data Architecture. But What is Data Architecture ?
In this article, you will learn What is Data Architecture, its components, frameworks, and best practices.
Table of Contents
What is Data Architecture?
Data Architecture is the blueprint for your data management strategy. The exponential growth of digital data demands agility- your business should be able to cope with the growing data and its needs. Defining a set of rules and protocols through Data Architecture can help you achieve it.
Data Architecture will define how data is acquired, stored, moved, queried, and secured. Also, the Data Architecture will also define the frameworks that can be used to manage data.
Why Data Architecture is Important?
An essential component of the data management process is a well-designed data architecture. Along with data engineering and data preparation, it supports efforts to improve data integration and quality. Additionally, it makes it possible for efficient data governance and the creation of internal data standards. Organizations can make sure that their data is accurate and consistent by doing these two things.
A data strategy that supports organizational objectives and top priorities are built on data architecture. Donald Farmer, the founder of the consulting firm TreeHive Strategy, stated that “a modern business strategy depends on data” in an article on the essential elements of a data strategy. Because of this, Farmer said, data management and analytics are too important to be left up to individuals. An organization must develop a thorough data strategy, supported by a solid data architecture, in order to manage and use data effectively.
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It provides a consistent & reliable solution to manage data in real-time and you always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.
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What are the benefits of Data Architecture?
A company’s ability to create efficient data analytics platforms that deliver insightful data and information is ideally aided by a well-designed data architecture. These insights enhance strategic planning and operational decision-making in businesses, which could result in improved business performance and competitive advantages. They also help with a number of other tasks, including medical condition diagnosis and academic research.
Among other advantages, data architecture also helps to increase data quality, simplify data integration, and lower the cost of data storage. According to Peter Aiken, a data management consultant and associate professor of information systems at Virginia Commonwealth University, it accomplishes this by adopting an enterprise perspective as opposed to domain-specific data modeling or concentrating on architecture at the database level.
What are the steps for creating the Data Architecture?
Creating a data architecture requires collaboration between data management teams, business executives, and other end users. If they don’t, it might not be in line with business objectives and information needs. Two of the nine steps for planning a data architecture, according to consultant Loshin, include talking to senior executives to gain their support and meeting with users to determine their data needs.
He suggested that organizations take the following actions, among others:
- based on data governance guidelines, assess the data risks;
- track data flows, data lifecycle information, and data lineage information;
- the infrastructure for data management technology should be documented and evaluated;
- and sketch out a schedule for the deployment projects for the data architecture.
Which are various roles are there in Data Architecture?
Data architects frequently take the lead in initiatives involving data architecture. They require a range of technical abilities in addition to the capacity to interact and speak with business users. A data architect spends a lot of time working with end-users to record business processes, the way that current data is used, and the need for new data.
On the technical side, data architects design their own data models and oversee the work of others who model data. They also create data flow diagrams, blueprints for data architecture, and other artifacts. Outlining data integration procedures and managing the creation of data definitions, business glossaries, and data catalogs are examples of additional responsibilities. In some companies, data architects are also in charge of choosing and evaluating technologies, as well as designing data platforms.
The following individuals are additional data management experts who frequently participate in the data architecture process:
- Data modelers: They also review business procedures and evaluate data requirements with business users. They then develop data models using the information they have gathered.
- Data integration developers: They are tasked with developing ETL and ELT jobs to integrate data sets after the architecture has been put into place.
- Data engineers: To get data to data scientists and other analysts, they construct pipelines. Additionally, they support data preparation efforts for data science teams.
What are the Components of Data Architecture?
After learning about What is Data Architecture, let’s deep dive deeper. The components of Data Architecture include data standards. Data standards are the fixed bar levels set for achieving a standard data response/activity in all situations.
These include defining a fixed schema for proper maintenance of data and defining security protocols to keep the data safe from hacks, data leaks, unauthorized access, etc.
Data Architecture must chalk out the data storage mechanisms. Some prefer to store their data On-premise while others prefer Cloud storage. Some use SQL and some use NoSQL databases. SQL Databases allow you to easily add data and piece it together in the form of tables.
NoSQL or non-relational databases can store unstructured data, grow much larger, and handle adding data dynamically to the database, whereas SQL databases could not. These are usually deployed On-premise and require infrastructure maintenance.
Some examples of SQL databases are MySQL, Oracle, MS SQL, Postgresql, etc. Some examples of NoSQL databases are MongoDB, Cassandra, Couchbase, etc.
The other options are Cloud Databases and Warehouses which have become more popular due to their Scalability. Since the databases and Warehouses are deployed On-Cloud, they save a lot of money on infrastructure and maintenance.
The architecture is always answerable for setting the data standards that characterize what sorts of data will go through it. Data Schema can help you achieve those standards. The data schema can be defined as follows:
Schema tells you how each entity should be collected and stored. Most companies usually define their data schema as per their requirements. Let’s consider a schema for contact info as an example. A contact entity/schema might include a name, phone number, email, and place of work.
The data types should be text data for name, integer data for a phone number, text data for email, and text data for place of work. Schema also tells you the relationship of that entity to others in the database, such as where it comes from and where it’s going.
We can use Data Security protocols for ensuring security across the organization, which can be visualized in the architecture and schema by demonstrating what data gets passed where, and when it travels from point A to point B, how the data is secured.
Security protocols can include:
- Encrypting data during travel.
- Restricting access to individuals in the form of authentication.
- Anonymizing data decreases the value of the information upon receipt by the receiving party.
- Additional required actions.
Data movement/ migration is the ability to move data starting from one place in your organization to the next through technologies that incorporate ETL, ELT, data replication, and CDC(Change Data Catch).
Your organization’s IT infrastructure and application landscape is ever-changing, and your applications need data from a large scope of channels.
This means you need proficient and secure Data Migration solutions to shift data across your systems without affecting the performance of your sources.
Data development, data synchronization, and data replication require Data Migration. Together, they empower you to maintain real-time data to keep your databases, Data Warehouse, Big Data, and Cloud systems current.
What are the Frameworks of Data Architecture?
Developers should be well aware of What is Data Architecture and its frameworks. Frameworks help you implement the standards and rules set in your Data Architecture.
You can use any of the following frameworks to handle your data:
A data Pipeline model shows how data travels from one point to another, from data ingestion to refining to finally where it is delivered. It covers the whole process of data ingestion from the client/application and how it moves along the pipeline (data streams or batch-processing) and finally how data reaches its final destination,i.e, where it is moved to, such as some application. Hevo Data is one such popular data pipeline.
Kubernetes( alternatively called K8) is an Open-Source automated management system. It handles automating deployment, scaling, and management of containerized applications.
It is ideal for hosting applications that require excellent Scalability because it achieves real-time data streaming through Apache Kafka.
API is an acronym for Application Programming Interface that a software/application uses to access data, server software, or different applications and has been around for a long while.
In layman’s terms, it is a software mediator that allows two applications to converse with one another. Consider an API a translator between two individuals who don’t speak the same language, however, can communicate using a go-between.
APIs use characterized protocols to empower developers to grow, connect and integrate applications rapidly and at scale. You can use APIs to fetch and send data over the web.
BI tools are used for Data Analysis and decision making. Some common features of Business Intelligence are:
- Predictive Analytics
- Data Mining
- OLAP, etc
Some popular BI tools are Tableau, Power BI, Sisense, etc.
The Best Practices for Data Architecture
After learning about What is Data Architecture, and how it works. Now let’s read about the best practices to follow to handle such things. With the help of the right Data Architecture, you can achieve consistency and manage your data efficiently. You can adopt the following best practices and use the right tools to set up your Data Architecture:
1. Ensuring Complete Data Integrity
For some organizations, Integrating, cleansing, and approving data from inside sources is an extraordinary affair, yet that is only the start.
Since the company should now depend on data coming from outside sources also, maintaining Data Integrity in your Data Architecture incorporates guaranteeing you have an approach to ingest data from outer sources, cleanse it, remove duplication when essential, and approve it.
2. Eliminating the Internal Data Silos
For building a data-driven business it is crucial to design the Data Architecture that supports Data Democratization( all the employees have access to data and make use of it). There are some organizations where information Silos are the standard.
Maintaining Data Silos is wasteful and is a demonstration of improper data management practices. This is because, at some point in the near future, when data is put away in different stores, individuals accidentally copy it and make changes after which nobody knows which information is truly right.
Data Architecture proves useful in separating those hindrances because it sets rules for data cleansing and validates it to verify that the data is exact and complete, as it is not of any use to the company.
3. Support for All Types of Data
The data we have at present mostly constitutes structured data that could be simply analyzed with standard tools or instruments. With the rise of Big Data and Cloud computing, the sheer volume of both structured and unstructured data has risen dramatically, and there’s crucial information for your company sneaking in all that data.
That implies that your Data Architecture should be built in such a way to allow data storage from numerous sources in different organizations, both structured and unstructured. Else, you are missing out on crucial information you need to know for making data-driven business decisions.
4. Implementing Solid Data Governance
Keeping up data quality is a continuous process and should never halt. Your Data Architecture should uphold that cycle at each step. This implies that your Data Architecture should be able to execute a robust data governance strategy for your enterprise.
While numerous organizations may just offer empty promises to the idea of genuine data governance, it is fundamental to build your Data Architecture to work with solid data governance. This way, you can feel positive about your data and depend on it to help you decide while making business choices that will give you an edge.
By now you must know What is Data Architecture and it plays a predominant role in achieving consistent data management. Data Architecture must take into reference all your data needs and show you the means and methodologies for achieving them. In this article, you learned about What is Data Architecture, its components, frameworks, and best practices.
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