How can you standardize data across different formats and platforms?

1 Why standardize data?

Data standardization has numerous advantages for data engineering, including reducing errors, duplicates, and outliers to improve data quality and consistency. It also enhances data interoperability and compatibility by allowing for data exchange and communication between different systems and applications. Furthermore, it simplifies data manipulation and aggregation to facilitate data analysis and reporting. Additionally, data standardization supports data governance and metadata management by enforcing data rules, policies, and standards.

3 How to standardize data platforms?

Data platforms are systems and environments that store, process, and serve data. These platforms can be classified into different categories, such as relational databases, NoSQL databases, data lakes, data warehouses, and data pipelines. To standardize these platforms, you need to identify the source and target data platforms and their characteristics, such as architecture, interface, query language, performance, scalability, etc. Additionally, you should pick a suitable tool or service to transfer data from one platform to another - Apache Sqoop, Apache Kafka, or AWS DataSync - and apply the transfer logic while monitoring the data movement and synchronization across platforms.

4 How to standardize data structures?

Data structures are the ways that data is organized, modeled, and related, varying depending on the data domain, business logic, and analytical needs. Examples of common data structures include tables, columns, rows, keys, indexes, graphs, trees, and arrays. To standardize data structures, you must identify the source and target data structures and their elements such as attributes, values, types, constraints, and relationships. Additionally, you should select a suitable tool or framework to transform data from one structure to another such as Apache Hive, Apache Pig, or AWS Athena. Finally, apply the transformation logic and verify the output data against the target structure elements.

5 How to standardize data quality?

Data quality is the measure of how well data meets the expectations and requirements of the data consumers and stakeholders. This quality can be evaluated by various dimensions, such as accuracy, completeness, timeliness, consistency, and validity. To standardize data quality, you must first identify the source and target data quality criteria and metrics like error rate, missing rate, freshness, duplication, and conformity. After that, choose a suitable tool or solution to assess and improve data quality - like Apache Nifi, Apache Atlas, or AWS Lake Formation - and apply the quality logic to evaluate the output data against the target criteria and metrics.

6 How to standardize data governance and metadata management?

Data governance and metadata management are essential processes and practices for data standardization, as they provide the rules, policies, and standards for data creation, modification, and usage. To standardize data governance and metadata management, you need to identify the source and target objectives and scope such as data ownership, stewardship, lineage, security, and compliance. Select a suitable tool or platform to implement and enforce data governance and metadata management, such as Apache Ranger, Apache Atlas, or AWS Glue Data Catalog. Lastly, apply the governance and metadata logic to audit the output data against the target objectives and scope.