Introduction to Data Architecture Principles
The set of rules and regulations given to manage the user’s data collection and management in a database where these rules help to keep the data framework in a consistent format is called Data Architecture Principles. This helps in forming the structure of data architecture in a better way so that data will be consistent and helps in integration well. The principles are important such that while managing the data architecture with all the available data assets, the principles help to maintain a logical view of the same without making compromises in the underlying structure thus helping the users to manage the structure.
What is Data architecture principles?
When we receive the data in the database, it is important to validate all the data given so that there will not be any discrepancy of data between source and target. If there are any anomalies, it is better to filter out the same with any automated tool. It is better to follow a pattern in naming the data columns in the entire team so that less confusion arises between the team members. Reports should be created for all the data entries so that time spent will be less on documenting the same later. Also, should not create data duplicates at all in the architecture.
- The place where data providers and buyers are bought together so they will have an insight into the data they are sharing is called the data marketplace. Initially, data providers used to get data from middlemen but nowadays, data is bought directly from the providers to reduce the confusion that happens about the origin and relevance of data. This place makes all the providers significant and hence newcomers will not feel left out in the marketplace.
- We have snowflake data marketplace, Otonomo, Datafairplay, Infochimps, and Qlik are some others where data normalization and data transaction happens very frequently. A catalogue is provided where we can look for the required datasets and ask providers to give necessary details for the data. Data subscription is readily available and mostly it is always, real-time data or batch data that has happened very recently. This can be used for data growth in a company and hence results in the potential profit of the company itself.
- Data marketplace provides various categories such as marketing, manufacturing, hospitality, and many others specific to the division where the data belongs. The location of the data is also provided and once the buyer requests access, certain rules and regulations should be signed up for continuous delivery of data in the marketplace for the same category of data.
Foundation of Modern Data Architecture
- While working with modern architecture, it is important to get away with all the old databases and legacy data modelling systems. We can move the data into open source databases by checking the efficiency and cost factors so that we will not lose much while working with old databases.
- It is better to move into managed databases so that time will not be spent on database administration and scaling up of the database. Security and backups will be taken care of by the database itself so that the users will get more time in working with data analysis and data modelling.
- A traditional data warehouse should be avoided as their storage capacity is less but a modern data warehouse has data lake storage where we can store both structured and unstructured forms of data in containers or blobs. This helps in data catalogues and analyzing the data where anyone can access the data easily.
- Microservices architecture is another advantage where several applications are built together and we can use the best database among them to work with our data. Hence, our database is not overburdened by data as there are several databases working together for the same goal.
- As a data lake acts as a repository for any kind of data, it is better to maintain an online catalogue and analyze the data as and when needed. This helps in predicting the future rather than digging the past always.
Data Architecture – Three phases
- Data must be always considered as a shared resource and hence barriers of regional, social and economic situations should not stand upfront while considering a dataset. This will help stakeholders to view data in a 360degree view to provide better insights.
- Security is very important while sharing data with stakeholders as the data will be mostly real-time. Hence, access to this data and providing security from the base level makes providers believe that their data is in safe hands and there will not be any malpractices involving this data.
- It is not a good practice to copy data to a new source. While doing so, resources are wasted and the fidelity of the data is compromised. Massive parallel processing is good in this case so that multiple workloads and multiple structures for the same data can be managed.
- Better understanding: Considering the data architecture rules, it is important to figure out all the data anomalies. This helps in understanding the data more and hence to take the right approach in dealing with such kind of data. Also, since the structure is built on top of data governance, people dealing with the data and the marketplace can be easily known.
- Different Tools: Since there is a better understanding on the data, it is easy for the analysts to use AI or Machine Learning tools. Integration is possible easily on the data and this helps in making better decisions over the data by the stakeholders. Latency of data in the database is very less after applying the rules as the hybrid environment takes over the data very well.
It is good to follow all the rules of data architecture from the start of data collection so that the work becomes easy and less tiring. This helps in ensuring the strategy of data architecture followed is right and hence customer demands can be met faster with higher efficiency.
This is a guide to Data Architecture Principles. Here we discuss the Introduction, What is Data architecture principles, Marketplace list, benefits respectively. You may also have a look at the following articles to learn more –