How do you combine different data types and domains?

1. Collate all the available data sources. 2. Create a data dictionary to understand the data points. 3. Bucket the high level Facts and Dimensions by defining the level of granularity and scheduling frequency based on business use case. 4. Perform ETL. Ingest data from available sources and standardize it by adding data quality checks via available could platforms and load it to a raw location. 5. Expose only required attributes to end user as and when required via materialized views or sophisticated user tables.

Combining data from different domains is crucial, but challenging. Structured, semi-structured, and unstructured data have different storage and processing needs. ETL tools like Apache Spark, Airflow, and AWS Glue can help. They can ingest data from various sources, standardize it, and load it into centralized repositories. This harmonization enables cross-domain analytics and ML, generating more comprehensive insights.

Understand the data. What are the different data formats, types, and domains? Convert the data to a common format. This will make it easier to process and analyze. Clean and prepare the data. Remove errors, duplicates, and outliers. Combine the data. Use data integration tools to merge, join, and match the data. Analyze the data. Gain insights by using data visualization tools.

1 - Identify the data types in all the source systems (Different domains). 2 - Suppose 4 different source systems (1-CSV,JSON or XML, 2 - RDBMS 3-Audio & Video, 4-Web API). 3- In Azure for example, Use Azure Data Factory (using Copy Activity) and linked services with all the sources, extract the data in Blob storage in JSON,XML or CSV format. for RDBMS directly fetch the data from source to Azure SQL Database stage table. 4 - Now analyze or standardize the extracted data applying models etc.

There are different ways to combine different data types and domains, depending on the context and the goal. Here are some examples: - If you want to merge user data from separate Google accounts, you can use the **data migration service or the Data Export tool. - If you want to combine two different data types in a data frame in R, you can either represent both types as strings or use a list instead of a vector. - If you want to enforce domain constraints in a database, you can use data type and constraints such as NOT NULL, UNIQUE, PRIMARY KEY, etc.

Before starting combing data it’s very useful to have a overview and a map of data sources and interactions between the datasets. I highly recommend Wardley Mapping technique to visualize any data sources in 2D. One axis’s is a value chain and a second is the data source (origin, custom made, product or commodity). This helps to recognize patterns and in data grouping.

For spatial data queries, analysis and conversion tools like ArcGIS or FME are very useful. Data harmonization in one data base with multiple input and output formats allows user to navigate data lakes easily.

Manual data integration is a method of combining data from different sources and domains by writing custom code. For example, I may use Python to connect to a MySQL database and a JSON file, and then use pandas to collect and transform the data. This method is good for simple or one-time integrations, but it can be hard to scale, maintain, and debug. To scale manual data integration, I use: - Consistent and standardized coding style and documentation. - Automation tools and frameworks, like Apache Airflow or Pentaho Data Integration. - Cloud-based services and platforms, like AWS Glue or Azure Data Factory. - Modular and reusable code.

I would personally recommend a domain-driven design when dealing with data from different sources. That ensures the proper level of abstraction and isolation, but also ups the time-to-productivity for analysts. Still, the advantages of DDD far outweigh the loss of productivity, as they directly impact (lower) the long-term complexity and cost.

Before combining data, it is crucial to understand its semantics, that is, its meaning. ?? This ensures that the combination is done in an accurate and meaningful way. ?? The quality of the data also plays a significant role in the analysis results. Therefore, it is essential to clean and validate the data before combining it. ?? Additionally, the scale of data needs to be considered while combining it. For instance, larger datasets may require the use of specialized tools and techniques. ??