How can you scale snowflake and star schemas?

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Snowflake and star schemas are two common ways of organizing data for data warehousing and analytics. They both use a central fact table that contains measures and keys to link to dimension tables that contain attributes. However, they differ in how they handle hierarchies and normalization of the dimension tables. In this article, you will learn how to scale snowflake and star schemas to handle large and complex data sets.

本文章的要点总结

Optimize infrastructure:

When scaling models, assess and enhance your data storage and memory capabilities. This ensures that as your data grows, your system can handle the volume efficiently without performance hiccups.
Embrace materialized views:

These precomputed views store query results and can speed up data retrieval in a star schema environment, making it a smart play for handling complex queries faster.

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Yuvaraj Birari

Top Voice Data Architect and Top Voice…
Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de…

1 Benefits and drawbacks of snowflake and star schemas

Snowflake and star schemas have their own advantages and disadvantages when it comes to scaling. Snowflake schemas normalize the dimension tables by splitting them into multiple levels of sub-dimension tables. This reduces data redundancy and storage space, but also increases the number of joins and the complexity of queries. Star schemas denormalize the dimension tables by keeping all the attributes in one table per dimension. This simplifies the queries and improves the performance, but also increases the data duplication and the risk of inconsistency.

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Taral Shah

Google Cloud, Data Analytics & Insights, Business Intelligence @ PwC
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1. Snowflake Schema: - Employ denormalization to consolidate related tables. - Enhance indexing strategies for optimized query performance. - Implement table partitioning based on key criteria for efficient data distribution. 2. Star Schema: - Distribute data across servers to evenly distribute the workload. - Utilize materialized views to precompute and store aggregated results. - Apply data compression techniques to minimize storage requirements and expedite data retrieval.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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1. SnowFlake Schema Benefits Reduction in data redundancy due to normalization, leading to more efficient storage. Maintenance tasks, such as updates to dimension attributes, are generally simpler due to the normalized structure.

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2 Factors to consider when scaling snowflake and star schemas

When you need to scale snowflake and star schemas, you must take into account various factors that can influence the design and performance of your data warehouse. These include the size and growth rate of your data, which may require more storage space, processing power, and partitioning strategies; the type and frequency of queries, such as analytical, operational, or ad-hoc; the level of detail and granularity of your data; and the quality and consistency of your data. Poor quality and inconsistent data can lead to errors in your data warehouse, affecting its reliability and usability. It is important to consider all these factors in order to ensure an effective data warehouse.

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Yuvaraj Birari

Top Voice Data Architect and Top Voice Data Governance!! DV2.0, Principal Data & Governance Architect, Led team of Data Engineers & Data Analysts, BI Architect, Data Architect
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Star Schema and Snowflake both modelling techniques are evolved from DWH problem statement. The requirement to provide fast reporting response (most in Sub-seconds) drives the Star Schema model. Least number of joins and fast Slice and Dice on key measures. Whereas Data Redunancy drives Snowflake model. Its bit of Normalization on highly denormalized Star-Schema. Mostly reference data is Normalized to make sure ease in future maintainability. To scale these models, you should consider, Data Volume, Future Facts, Conformed Dimensions, Infrastructure - Storage & Memory, Distribution of processing etc... You can also add, data archival in Information Life Cycle.

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Graham P.

Helping shape data-enabled organisations to deliver tangible business value
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Data vault is one modelling approach I feel adds scalability and better visibility of data lineage. You can also sit star schemas on top of a DV with an added benefit of enhanced flexibility when the level of granularity needed for analytics/reporting purposes changes... I.e accommodates change more easily and seamlessly

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3 Best practices for scaling snowflake and star schemas

To scale snowflake and star schemas effectively, you should follow some best practices that can help you optimize your data warehouse design and performance. For instance, it is important to choose the appropriate schema type for your data and queries, as snowflake schemas are more suitable for data sets with many attributes, hierarchies, and relationships, while star schemas are more suitable for data sets with fewer attributes. Additionally, indexing, partitioning, and compression techniques can improve query speed and storage efficiency. Moreover, caching, materialized views, and aggregation tables can reduce query workload and complexity. All of these best practices can help you achieve better performance from your data warehouse.

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Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de dados | Desenvolvedor de Machine learning | Inteligência artificial | Business Intelligence | Python | SQL | Power BI | PhD Student
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Antes de desenvolver um modelo de dados, é preciso compreender as necessidades de negócio, que te levará a um modelo conceitual (MER), a partir dele, você vai para um modelo lógico, direcionado a um star schema ou snow flake e, isso dependerá das necessidades de negócio, aliada as técnicas de modelagem que utilizará. O modelo de dados precisar ter aderência às necessidades de negócio e, naturalmente, técnicas de modelagem s?o utilizadas para você potencializar seu trabalho. Se estiver utilizando um banco de dados relacional para o seu modelo, existem algumas técnicas, como: fato agregada e view materializada Tb existem tecnologias específicas para isso, como Analysis Services multidimensional, que já garantirá agrega??es no seu modelo

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4 Tools and frameworks for scaling snowflake and star schemas

When scaling snowflake and star schemas for data warehousing and analytics, there are many tools and frameworks available. Snowflake is a cloud-based data warehouse platform that supports both snowflake and star schemas, with features such as automatic scaling, data sharing, and data protection. Star Schema Benchmark (SSB) is a benchmarking tool to measure and compare the performance of different data warehouse systems and schemas using a star schema model. Apache Spark is a distributed computing framework for processing and analyzing large and complex data sets from various sources, formats, and schemas. Apache Hive is a data warehouse software that allows you to store and query large structured data sets with SQL-like language, including snowflake and star schemas.

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5 Challenges and solutions for scaling snowflake and star schemas

Scaling snowflake and star schemas can present some challenges, such as data quality and consistency. To ensure accuracy and reliability, data validation, cleansing, transformation, and reconciliation must be implemented. Additionally, data security and privacy measures like encryption, authentication, authorization, and auditing must be put in place to protect data from unauthorized or malicious actions. Lastly, data governance and management policies such as data cataloging, metadata management, data lineage, and data lifecycle must be established to define and control the access, usage, and quality of your data.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de dados | Desenvolvedor de Machine learning | Inteligência artificial | Business Intelligence | Python | SQL | Power BI | PhD Student
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Desenvolver um modelo de dados, seja ele relacional ou dimensional. Seja normalizado ou desnormalizado. Seja qual for o nome que você quiser dar. é preciso entender que estamos construindo o BACK END de um sistema de apoio à decis?o, onde precisamos projetar uma arquitetura de solu??o/dados, para que sejam possível disponibilizar informa??es que apoiar?o a decis?o, baseadas nos requisitos funcionais mapeados. Recomendo muito que estude a Engenharia de Software e Sistemas de Apoio à Decis?o, pois certamente potencializará sua trajetória profissional. Dúvidas? Chamaí!

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How can you scale snowflake and star schemas?

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3

4

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6

1 Benefits and drawbacks of snowflake and star schemas

2 Factors to consider when scaling snowflake and star schemas

3 Best practices for scaling snowflake and star schemas

4 Tools and frameworks for scaling snowflake and star schemas

5 Challenges and solutions for scaling snowflake and star schemas

6 Here’s what else to consider

Data Warehousing

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How can you scale snowflake and star schemas?

1

2

3

4

5

6

1 Benefits and drawbacks of snowflake and star schemas

2 Factors to consider when scaling snowflake and star schemas

3 Best practices for scaling snowflake and star schemas

4 Tools and frameworks for scaling snowflake and star schemas

5 Challenges and solutions for scaling snowflake and star schemas

6 Here’s what else to consider

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