Here's how you can manage large volumes of data in Data Warehousing.

Data models are crucial for organizing data in large warehouses for efficient storage, retrieval, and analysis. They should consider data granularity, consistency, and materialization to improve query response time. Data modelers must consider permissions, governance, and compliance with regulations. Star Schema, a structure consisting of fact tables and multiple dimension tables, is commonly used for large data handling and OLAP queries, while Snowflake Schema reduces redundancy and optimizes storage space.

Data modeling may sound like an unnecessary discipline in the modern data world, but it remains a critical discipline in a successful data management strategy. It can help with large volumes as well. Most standard reporting is done at a summary level. I have always designed top down starting with the summary layer I need, then bringing it down into the details. The summary layer should be tabled or materialized views for optimal performance. Physical data modeling was critical with Oracle on-premise databases, and good physical data modelers or DBAs were prized. Now that most DW platforms have gone to the cloud, there is some automated scalability offered there that takes some of the burden away, but good data modeling will help.

As a Data Engineer, I must always keep in mind the importance of Query Optimization when dealing with large volumes of data in Data Warehousing, as I will often need to query large tables when generating reports. When designing a Data Warehouse, we must consider that the data will grow exponentially. Querying a table with large volumes of data can be challenging. This is where the concept of Query Optimization comes into play, helping us implement mechanisms like indexing and partitioning to speed up the retrieval of records from large tables. Additionally, simplifying complex queries by removing unnecessary columns from SELECT statements and using subqueries efficiently can further enhance performance.

I had to remind myself recently of a lesson I was taught eons ago - start with looking at the query profile. It speaks volumes and there are often surprises.

Lately we started to use Fabric and Databricks to use large volume of data . We must build CDC to load only incremental that only loads data which is new and updated. Also both technology issues delta lake in parquet files which are compressed and optimized to load incremental data. Finally we use Lake-house architecture to consume data what is needed and only loaded once with ETL approach.

The early part of my career was primarily on Oracle. I was part of teams that handled millions of dimension rows and billions of fact records. Oracle offered a variety of performance tuning features such as indexing, partitioning, query tuning, materialized view, and others. Tera data was also there for particularly large orgs. Then came Netezza - a DW appliance. These latter types of solutions were optimized for data warehouses and made some of the tuning more of a turnkey. Recently, I have been working with Snowflake. My impression starting out was that there would be less of a need for tuning here. We were caught by surprise, though, when query profiling revealed a bottleneck🙃 So, some of the old techniques are still of value.

As a data engineer, manage large volumes of data in data warehousing by using distributed computing frameworks like Apache Hadoop or Spark, optimizing storage with columnar databases like Apache Parquet, and implementing data partitioning and indexing for efficient querying and retrieval.