Traditional approaches to building data warehousing often result in the creation of disparate “silos of information” where important insights are inaccessible for analysis and decision making. Modern storage concepts, such as Data Warehouse, Data Lake and their hybrids, can unify disparate information and make it available in a centralized way for data streaming and business intelligence. It is not only important to choose the right storage architecture, but also to implement Data Governance) and Data Minimalism) to prevent storage from becoming uncontrollable Data Swamps).
To summarize this part, it is worth highlighting the main practical steps that will help you apply the concepts discussed to your daily tasks:
- Select efficient data storage formats
- Move from CSV and XLSX to more efficient formats (Apache Parquet, ORC) for storing large amounts of data
- Implement a data versioning system to track changes
- Use metadata to describe the structure and provenance of information
- Create a unified company data architecture
- Compare different storage architectures: RDBMS, DWH, and Data Lake. Choose the one that best meets your needs for scalability, source integration, and analytical processing
- Design a process map for extracting, loading, and transforming data (ETL) from various sources for your tasks. Use visualization tools such as Miro, Lucidchart or Draw.io to visually represent key steps and integration points
- Implement Data Governance practices and Data Minimalism
- Follow the Data Minimalism approach – store and process only what is truly valuable
- Implement Data Governance principles – define responsibility for data, ensure quality and transparency
- Learn more about data management policies and DataOps concepts, VectorOps
- Define data quality criteria and procedures for data validation within DataOps
Well-organized data storage creates the basis for centralizing the company’s analytical processes. The transition from chaotic accumulation of files to structured storages allows turning information into a strategic asset that helps to make informed decisions and improve the efficiency of business processes.
Once the processes of data collection, transformation, analysis and structured storage are automated and standardized, the next stage of digital transformation is the full-fledged work with Big Data.
