Building a PostgreSQL Data Warehouse: A Comprehensive Guide

To better understand the highly fragmented marketing data landscape, today’s enterprises need tools and technologies that demystify market insights using a data-driven approach. The data today’s enterprises possess has a hidden value — empowering them to generate more intelligent insights. The goal is to make business decisions better and quicker than your competitors.

Hence, to help organizations make informed decisions, a data warehouse is a must. It stores data and analyzes it later. PostgreSQL Data Warehouse can be leveraged to achieve this. Moreover, it’s valued for its advanced and open-source solution that provides flexibility to business processes in terms of managing databases and ensuring cost efficiency.

This blog post will discuss how to use and run Postgres Data Warehouse, its features, benefits, limitations, and Challenges. Let’s begin.

What is PostgreSQL?

PostgreSQL — an open-source relational database management system (RDBMS) — offers today’s data industry top-of-the-class performance optimization and advanced datatypes features. 

Thanks to its feature-rich suite experience robust and reliable performance, PostgreSQL ranks the 4th most popular database management system worldwide.

On the other hand, PostgreSQL operates flawlessly with all major operating systems like Linux, Windows, and Mac OS. PostgreSQL offers multi-application interoperability. The platform provides its users with a seamless way to manage databases, a high degree of control, and flexibility.

Some key features of PostgreSQL are as follows:

• It’s open-source, hence free: PostgreSQL is an open-source RDBMS. It’s easy to download and compatible with all major operating systems, making it suitable for a young team of developers in a startup.
• It’s known for its diverse user base: Apple, Cisco, Etsy, Facebook, Instagram, Red Hat, Skype, Spotify, and Yahoo — all leverage PostgreSQL for their everyday database needs. 
• It’s highly reliable: In PostgreSQL, the write-ahead logging makes it fault-tolerant. Moreover, high compatibility with multiple data types makes PostgeSQL’s use cases synonymous with industries’ pain points.
• And, it’s secure too: PostgreSQL offers a robust access control system and follows all major security compliance frameworks. To include a few, PostgreSQL is LDAP, GSSAPI, SSPI, etc.. 

What is a Data Warehouse?

Data warehouses are subject-oriented, integrated, time-variant, non-volatile, summarized, and especially, the single source of data truth. For reporting and analysis purposes, enterprises store data — like employee data, customer data, sales data — in the data warehouses. The legacy hardware systems, or the on-premises data warehouses, have massive IT reliance and almost no self-service potential, as far as marketers’ are concerned, prompting many to move their data warehousing to the cloud.

A data warehouse provides a wide range of technology options. It takes advantage of the core offerings that empower its users to command their business needs — like quick scalability, user-friendliness, flexible storage, and limitless computational capacity. The users can deploy multiple sources as per their needs and only pay for what gets used.

How to use and run PostgreSQL Data Warehouse?

Typically PostgreSQL is row-oriented but can work with analytical queries. Although PostgreSQL is considered the best in its field, better ways to manage data warehouses exist. In this PostgreSQL data warehouse tutorial, we will discuss concerns (some ground rules), which will help you better understand how to use and run the PostgreSQL as Data Warehouse.

But, before starting, there’s a word of caution.

Note: It’s not recommended to use the production Postgres instances for data metrics/reporting. As the work volume differs for analytics, it can have performance implications on a production system.

It’s recommended to avoid CTEs

CTEs (Common Table Expressions), termed ‘WITH’ queries, are the preferred way to exclude deeply nested subqueries. 

WITH my_expression AS (
    SELECT customer as name FROM my_table
)
SELECT name FROM my_expression

The PostgreSQL will compute CTEs, aggregate the result, and scan content when used. The process can slow down the production system. The preferred way to write a subquery in PostgreSQL, before version 12, will look like this:

SELECT name FROM (
    SELECT customer as name FROM my_table
)

When & When Not to Use Indexes

Generally, indexes are not that important when for analytics workload, compared to the production queries. And, cloud data warehouses like Redshift and snowflake don’t have them. 

But, when to use an index? The answer is simple; queries work much faster with indexes. And here are some reasons why not to use indexes:

1. A table scan is a much faster way to work with analytics queries than an index scan.
2. The index has the tendency to increase the total space used by increasing the size of the table. So, in that terms, it’s not very storage efficient.
3. An extra cost is charged on every insert/update when indexes are used.

To Increase Table Scan Performance, Use Partitioning

Partinioning can significantly increase the table scan performance, that too, without paying an extra storage cost. Partitioning breaks down one big table into several small ones, making it incredibly easy and fast to read queries. For example, if you are breaking tables up by time, it’s recommended to use’ range partitioning.’ The process will break down data from years to months, which is, in fact, much easier to follow.

It’s recommended to Minimize Disk & I/O

Disk I/O is important because, most of the time, table scans are preferred. Hence in the order of importance, see the below-written comments:

1. There should be enough memory in the cache such that PostgreSQL can access tables.
2. SSD should be preferred over hard drives.
3. Check for I/O’s availability, as some cloud data warehouse providers would increase I/O based on database reading.

After Bulk Inserts, Don’t Forget to Vacuum

No doubt, vacuuming is important because it helps run PostgreSQL run smoothly. It saves space, but when running the ‘vacuum analyze’ function, it computes statistics and ensures the query planner estimates everything. Nevertheless, PostgreSQL runs on an auto-vacuum process.

It’s recommended to run the ‘vacuum analyze’ function only after a but upload has been done. This process will ensure efficient queries to run for new statistics immediately. 

Parallel Queries Are Ideal for Warehousing Applications

The president is that the parallel queries have been termed the best practice for the PostgreSQL data warehouse. Although these queries add latency to the processes, they don’t affect your analytics workload because queries will take multiple seconds to execute.

To see whether the parallel queries are running, use the ‘explain function.’ Did you notice ‘Gather’ followed by some parallel queries (join, sort, index, scan, seq scan, etc.)?

->  Gather Merge  (cost=2512346.78..2518277.16 rows=40206 width=60)
    Workers Planned: 2
    Workers Launched: 2
        ...
      ->  Parallel Seq Scan on activity_stream s_1 

‘Workers’ is the term that defines the number of processes executing the work in parallel. Two settings control the execution: Max parallel workers & max parallel workers per gather. Look in the example given below:

show max_parallel_workers;            -- total number of workers allowed
show max_parallel_workers_per_gather; -- num workers at a time on the query

Use ‘explain(analyze, verbose)’ to know how much time each worker spent and the number of rows it processes.

Worker 0: actual time=13093.096..13093.096 rows=8405229 loops=1
Worker 1: actual time=13093.096..13093.096 rows=8315234 loops=1

Increase Statistics’ Sample Space

Statistics are collected on a table then updated to the query planner. In the PostgreSQL Data warehouse, statistics are done by sampling the tables and storing the common values. The level of accuracy depends upon the number of samples it takes in.

Hence, on a per-column basis above-written can be executed as follows:

ALTER TABLE table_name ALTER COLUMN column_name set statistics 500;

And, for an entire database:

ALTER DATABASE mydb SET default_statistics_target = 500;

Reduce the Number of Columns

It’s essential to be aware of the storage structure in PostgreSQL. It uses sequential row-based storage on disk. This implies that the entire first row, inclusive of all its columns, is stored before moving on to the next row.

Consequently, when extracting a limited number of columns from a table with numerous columns, PostgreSQL loads unnecessary data. Since the table data is read in fixed-sized 4 KB blocks, it cannot selectively read specific columns from a row on disk.

In contrast, many dedicated data warehouses utilize columnar stores, allowing them to read only the necessary columns efficiently.After learning about how to build a data warehouse in postgresql, now lets learn about its benefits.

PostgreSQL Data Warehouse: Benefits

Postgres as Data Warehouse leverages OLTP and OLAP to manage streamlined communications between databases. For example, it’s easier to store the data and communicate with databases using OLTP using OLAP. These features make PostgreSQL an organization’s favorite for OLAP as a data warehouse.

Data warehouse with Postgresql involves capabilities to handle large volumes of data  and efficiently support analytical queries. PostgreSQL for data warehouse offers a balance between relational database features and analytical processing capabilities.

But some conventional benefits of PostgreSQL Data Warehouse are as follows:

High Compatibility

Operations in PostgreSQL Data Warehouse is compatible with any kind of programming language — from Python to Java and C++.

It’s Open-source; Hence Free

PostgreSQL is open-source software and fairly easy to download and execute. It’s perfect for young startups and data professionals.

Highly Scalable & Known for Optimized performance 

The PostgreSQL data warehouse services are built to scale, as you can add as many nodes you want or as per your business requirements. On the other hand, PostgreSQL is known for its optimized performance compared to conventional databases. PostgreSQL works well under different scenarios.

PostgreSQL Data Warehouse: Limitations & Challenges

PostgreSQL for a data warehouse is the popular choice, but it’s hard to manage, and challenges do come at the start. Hence, some limitations for PostgreSQL Data Warehouse are as follows:

Bugs & Dependencies

Compatibility with all your programming and hardware processes is necessary. But, sometimes, it will not be the case and can confuse engineers.

Security Concerns

PostgreSQL Data Warehouse is an open-source solution; hence security vulnerabilities exist because PostgreSQL source code is readily available to all. 

Load-balancing turns tricker at scale

Today, organizations use the load-balancing technique to balance operations between multiple databases. Due to that reason, data warehousing using PostgreSQL becomes complicated and requires an easy way out.

A lack of support for urgent technical issues

In case a critical PostgreSQL production problem requires urgent resolution and you are uncertain about the solution. This situation often leads to rigorous online searches and attempts at trial-and-error fixes. You can reach out to the community for assistance, which is an option. But, there is a risk of not receiving timely responses, intensifying the urgency of the situation.

Conclusion

In this blog post, we learned how PostgreSQL can be leveraged as a data warehouse after some tweaks in the work processes. And, if you are on a tight budget but also want high performance, the PostgreSQL Data Warehouse would be a great choice, which can be considered a worth-mentioned choice to your CTO.

But, you are from non-technical background or new in the game of data warehouse and analytics, Hevo Data can help!

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