Many organizations start their data management practices with a single database—and this makes lots of sense, at first. But today, enterprises are amassing data at an impressive rate. Thanks to AI and machine learning-powered solutions, we create 402.74 million terabytes of data every day. 

Think about adding books to a library every single day. Even the most competent librarian will struggle to find the right book after a while—which is why they segment the library into sections and why the Dewey Decimal System is so helpful. Rather than scouring every shelf, you only have to look at a handful of books to find the one you’re after.

Data partitioning is a lot like that. It helps organize your data so each query is limited to a “shelf,” or a particular segment of data. As your enterprise grows, your data needs grow and a single database isn’t efficient—and that’s where data partitioning is useful. Here’s what you need to know about implementing data partitioning successfully. 

The Importance of Data Partitioning 

Data partitioning—sometimes also called database partitioning—is a data management strategy geared toward achieving three things:

• Increasing efficiency
• Improving scalability
• Boosting performance

The process of partitioning data involves breaking up datasets into smaller segments to make large volumes of data more manageable. As a result, data is processed faster and more efficiently. 

This kind of partitioning can be invaluable for modern enterprises, especially as solutions like big data analytics, cloud computing, AI, machine learning, and applications powered in real-time become increasingly prevalent. All of these technologies amass massive scores of data—and partitioning eases the load by distributing this data and breaking it up into more manageable segments.  

So, where does all this data go? With partitioning, data is spread across nodes and servers, easing the overall workload. This optimizes query performance and makes the most of an enterprise’s resources, and can even make cloud environments more cost-effective. What’s more, data partitioning:

• Improves responsiveness for applications running in real-time
• Assures high levels of availability
• Limits the risk of downtime caused by a single point of failure
• Supports parallel data processing

Essentially, data partitioning is a smart, modern approach to database management; one that helps companies keep their operations running smoothly amidst burgeoning data growth, an increase in data-driven solutions, and the growing need for scalability.

There are certainly many benefits to data partitioning, but how does it work? How does data partitioning improve your operations?

Partitioning optimizes your data by dividing it all up into smaller, more manageable portions. As a result:

• Data retrieval times are sped up since there’s far less data that needs to be scanned
• Concurrency, or the ability for your system to perform multiple processes at once, increases since queries can happen simultaneously in different partitioned segments. 
• Data management is more streamlined since it’s broken into more management partitions.
• Your overall system is far more reliable since a failure in one segment doesn’t impact your entire database.

Strategies for Data Partitioning on a Large Scale

The first consideration to partition data on a large scale is the approach you’ll take for how you plan to segment all of the data. There are several methods for data partitioning, and the strategy you choose should largely be informed based on how you’ll access this data. 

Vertical Partitioning

The first approach, vertical partitioning, breaks data up into columns, based on commonalities within each column. This may be a useful strategy if your database contains a large volume of data arranged in a substantial number of columns, but you only need to access one subset of columns at a time. Benefits of vertical partitioning include:

• Reduced duplicate data and therefore a reduction in total required storage space.
• Improved search query performance when you only need to scan a particular subset of columns.
• Simpler data management.

Vertical partitioning is especially useful if your database performs queries within some columns more than others. For example, if you have a products data table with many columns of products but only need to access a few columns frequently. 

In this instance, you can use vertical partitioning to segment the more frequently accessed product columns (like product name, price, and description) separately from the product columns that you don’t need to query as often (product speculations, manufacturer information, or reviews, for example).

It’s important to note that vertical partitioning may make data updates a bit more complicated in some situations, and may also necessitate additional joins when you need to retrieve data from more than one partition at a time.

Horizontal Partitioning

Conversely, horizontal partitioning, sometimes also referred to as “sharding,” segments data into smaller, isolated pieces—shards—that hold a subsegment of data based on preset partitioning rules. Horizontal partitioning is often chosen for expansive data tables that contain large volumes of data. Benefits of horizontal partitioning include:

• Improved query performance due to less data overall that needs to be scanned for each query
• Simpler data maintenance and easier management
• The ability for horizontal scaling means your database can take on an increased data volume and an increased user workload.

Horizontal partitioning is helpful for situations in which you have a large table with thousands—or even millions—of rows (like orders, for example), and you want to bolster query performance. In this case, horizontal partitioning can break these rows down into smaller shards based on data in each row (like with a customer ID row). With this partitioning strategy, queries are faster and more efficient because the database only queries a certain shard of data.

Horizontal partitioning may not be the right strategy for your database—or may require additional setup—if you need to retrieve data from multiple shards at the same time. Additionally, a horizontal partitioning approach may lead to data fragmentation if it’s set up improperly.

Additional Partitioning Strategies

Databases are not limited to horizontal or vertical partitioning, however. Other data partitioning strategies include:

• Range partitioning, which allows you to divide a data table up more independently, based on specific columns and a certain range of table values. This may be useful if you need to access data based on information like order/purchase dates or customer IDs.

Range partitioning is most helpful for data that is structured by order. It needs to be set up carefully to avoid data fragmentation.

An example of successful range partitioning would be improving query performance by segmenting all orders in a table to query based on sales date.

• List partitioning allows you to break up a data table into individual segments based on a list of values in a column. It’s a good strategy when you have data that’s often accessed at the same time.

One use case for list partitioning would be when you have a data table for customers but would need to query these customers based on a particular region or department. 

• Hash partitioning takes the approach of breaking a table up into independent portions based on a hash function within one column. It makes sense for data that isn’t ordered—and even allows you to distribute data evenly across more than one partition.

A use case for hash partitioning would be to distribute a users table with a username column across multiple partition segments. 

• Composite partitioning is another option that gives you the choice to combine several partition strategies for improved performance—even with complex queries or complicated data structures.

harpin AI: The Simplest Way to Supercharge Large Volumes of Data

Especially when it comes to large volumes of data, multiple datasets, and partitioned segments, you need a solution to unify all this data, eliminate redundancies and fragmentation, and identify errors.

harpin AI brings all your datasets and systems together. With today’s technologies and data management practices, customer and identity data is scattered across multiple systems, including your CRMs, CDPs, POS platforms, and more. Through clustering, harpin AI stitches your data together, connecting events for each customer, even across a range of systems. 

The benefit of identity resolution with harpin AI is that it’s easier than ever to bring this data together. Gone are the days of intensive computation and inaccurate or scattered customer profiles. This means:

• Customer profiles come together across your entire enterprise
• Updated, cleaned up, and accurate customer data
• No more redundancies and duplicate records

Once harpin AI is set up in your system, data is continuously filtered through our AI/ML tools and algorithms to proactively validate identity data in real time. So you can always trust that new data is pristine and existing issues will be fixed efficiently, saving you time and money—and with harpin AI, cleaner data can boost your ROI by 40%

Want to learn more? Book a demo today!