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What are the best data partitioning strategies for high-performance data warehousing?

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Data partitioning is a technique that divides a large table or dataset into smaller, more manageable units, based on some criteria. It can improve the performance, scalability, and availability of data warehouses, especially for complex queries and large volumes of data. But how do you choose the best data partitioning strategy for your data warehouse? In this article, we will explore some of the most common and effective data partitioning strategies, and their benefits and drawbacks.

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Nicholas Leong

1 Horizontal Partitioning

Horizontal partitioning, also known as row partitioning, splits a table or dataset by rows, based on a partition key or expression. For example, you can partition a sales table by date, region, product, or customer. Horizontal partitioning can reduce the amount of data scanned by queries, improve parallelism and load balancing, and isolate failures or maintenance tasks. However, horizontal partitioning can also increase the complexity of data loading and querying, and require careful planning and tuning of the partition key and size.

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Nicholas Leong
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Horizontal partitioning can help optimize computational resources. To do this effectively, analyze the queries you frequently run or expect to run, and identify patterns that can guide you in strategically partitioning your data. For example, if you have a daily batch job, consider partitioning your table using the date/timestamp column. This assumes that your downstream queries will include a date condition in the WHERE clause to avoid scanning irrelevant data. Keep the distribution of your data in mind when selecting a partition column. If all of your data falls in one day, then date-based partitioning won't be beneficial. Aim for a partitioning strategy where data is nearly uniformly distributed across partitions.

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Keyur Shah

Data Architect | Data Engineering | Data Warehousing | Analytics & BI | Azure Certified | AWS Certified
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Horizontal partitioning is suitable when data is stored in columnar file formats like Parquet or ORC. This is common pattern for storing big data.

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Arul S

Senior Engineer @ DBS Bank
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Horizontal partitioning enables horizontal scaling by adding more servers to the database cluster. As the dataset grows, you can add more partitions and distribute the data across additional servers to handle increased load.

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2 Vertical Partitioning

Vertical partitioning, also known as column partitioning, splits a table or dataset by columns, based on the frequency or type of access. For example, you can partition a customer table by separating the frequently accessed columns (such as name, address, phone) from the rarely accessed columns (such as preferences, history, feedback). Vertical partitioning can reduce the amount of data transferred and stored, improve query performance and compression, and enable columnar storage and processing. However, vertical partitioning can also increase the number of tables and joins, and complicate data integrity and consistency.

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3 Hybrid Partitioning

Hybrid partitioning, also known as composite or nested partitioning, combines horizontal and vertical partitioning, to create multiple levels of partitions within a table or dataset. For example, you can partition a sales table by date (horizontal) and then by product category (vertical). Hybrid partitioning can offer the advantages of both horizontal and vertical partitioning, and allow more flexibility and granularity. However, hybrid partitioning can also increase the overhead and complexity of data management and querying, and require more metadata and indexes.

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4 Hash Partitioning

Hash partitioning, also known as random or uniform partitioning, splits a table or dataset by applying a hash function to a partition key or expression, and distributing the rows or columns evenly across a fixed number of partitions. For example, you can partition a customer table by hashing the customer ID and assigning each row to one of four partitions. Hash partitioning can improve the balance and distribution of data and workload, and avoid skewness and hotspots. However, hash partitioning can also make queries less selective and efficient, and require more resources and coordination.

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Keyur Shah

Data Architect | Data Engineering | Data Warehousing | Analytics & BI | Azure Certified | AWS Certified
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Hash partitioning may not distribute records evenly across a fixed number of partitions. The distribution depends on the histogram of the partition key. Some partitions will get higher number of records than others for a skewed histogram.

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5 Range Partitioning

Range partitioning, also known as interval or bucket partitioning, splits a table or dataset by defining a range of values for each partition, based on a partition key or expression. For example, you can partition a sales table by defining a range of dates or amounts for each partition. Range partitioning can simplify data loading and querying, and support partition pruning and elimination. However, range partitioning can also create uneven partitions and skewness, and require manual maintenance and adjustment.

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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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What are the best data partitioning strategies for high-performance data warehousing?

1

2

3

4

5

6

1 Horizontal Partitioning

2 Vertical Partitioning

3 Hybrid Partitioning

4 Hash Partitioning

5 Range Partitioning

6 Here’s what else to consider

Data Engineering

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What are the best data partitioning strategies for high-performance data warehousing?

1

2

3

4

5

6

1 Horizontal Partitioning

2 Vertical Partitioning

3 Hybrid Partitioning

4 Hash Partitioning

5 Range Partitioning

6 Here’s what else to consider

Data Engineering

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