How can you optimize data modeling in columnar databases?

é muito importante a análise detalhada dos banco de dados colunares e como s?o otimizados. Dependendo do cenário de aplica??o alguns bancos de dados s?o mais indicados e iremos obter uma melhor performance. A avalia??o de custos e aderência a infraestrutura de Cloud existente é fundamental para a escolha mais adequada.

?? A clarification: Apache parquet is a data file format and don't support SQL transactions itself; and mongodb supports an index type with columnar representation but its data is document type. ? Some tools; include non-columnar databases; as said previous can mimic some features to improve performance or scaling; but a columnar database is more complex than the above! ?? Check if you need to columnar database or non-columnar database with specific group of columnar features.

Other factors to consider when picking a columnar database are the connectivity with your data sources and the expertise within your organization towards the tool. For example, Big Query offer very easy to use connectivity with Web analytics tool GA4 and Google web advertising platforms such as Google Ads, Campaign Manager or Search Ads 360.

Normalized schema is not always your default choice especially for fact table. JOIN is you enemy! Try to leverage nested structures instead. For instance do not create a table for basket transaction header and a table for basket lines Create a basket table at the header level having a repeated field for its lines. So you remove your need to JOIN the data It will divide - you storage by 2 - you volume of read data by 3 when queried - you compute need by 10+ Of course those numbers will vary according to your use case but you got the point Note: do not abuse of the nested structure, it is hard to maintain and adds complexity. You must reserve this for fact table with inserts only (no updates/no deletion)

This type of database does not use rows as the physical presentation of the data; for this reason there are some considerations to take into account: ?? One table support a several similar business cases or similar access patterns as possible. ?? Avoid high amount of joins in query as possible. ?? Nested fields; like JSON or arrays; can help you so much. ?? Avoid having too many nulls in the columns.

Combining compression and partitioning in columnar databases serves as a powerful strategy for optimizing storage and query performance. Compression, achieved through algorithms like run-length encoding or dictionary encoding, minimizes data footprint, conserving storage space. Concurrently, partitioning based on criteria such as date or category organizes data into manageable segments, facilitating efficient query processing. This synergy not only reduces storage costs but also enhances overall database performance, offering a balanced approach to data management.