Amazon Redshift is great for real-time querying, but it's not so great for handling your ETL pipeline. Fortunately, Xplenty has a highly workable solution. Xplenty can be used to offload ETL from Redshift, saving resources and allowing each platform to do what it does best: Xplenty for batch processing and Redshift for real-time querying.
Redshift is Amazon’s data warehouse-as-a-service, a scalable columnar DB based on PostgreSQL. Xplenty integrates with Redshift, among other databases, and can read data from countless sources, including SQL Server, MongoDB, and SAP HANA.
In this guide, we'll go through the benefits of ETL, why you shouldn't depend on Redshift for it, and how you can start using Xplenty for better results all-around.
Table of Contents
- What Is ETL?
- Why Use an ETL Tool?
- What Is Amazon Redshift?
- Using Amazon Redshift as Your ETL Solution
- The Downsides of Amazon Redshift ETL
- How Does Offloading Redshift Data Work?
- Loading Data From Redshift
- Storing Results in Redshift
- Easily Offload Your ETL From Redshift
What Is ETL?
ETL means "Extract, Transform, and Load" data. Using an ETL tool, you can collect and process data from several sources into one data store where you can analyze the data at a later point.
Most companies have many data sources, but the data is presented poorly within each source. Since the data is separated and disjointed, analyzing it all as one is difficult without the use of ETL. The ETL process allows you to quickly analyze complete results using one report, informing your business decisions and overall strategy.
Why Use an ETL Tool?
Businesses can justify many use cases for an ETL tool as the benefits of ETL include the following.
Manage Big Data
Very large data sets are easy to handle with ETL tools, which can combine structured and unstructured data from multiple sources. Such tools can also prepare large data sets that aren't being stored in data warehouses so that you can use the information with integration solutions.
Your ETL solution should provide your business with quick access to the transformed and integrated data. Without it, your business will miss out on timely, critical insights that can inform decisions. By the time data is loaded into your data store, it is ready to use, allowing BI applications to run queries and generate a report without the need to join records, format, or perform calculations.
Visual Data Flow
A modern ETL application like Xplenty will allow your business to take advantage of a graphical user interface (GUI), allowing your users to visualize the design process with little to no programming knowledge. This is a major advantage and also provides a greater understanding of the logic that's informing the data glow between source systems and the data warehouse. Several people can collaborate on the design of the data flow, too.
Leverage Existing Framework
Tools like Xplenty are built from the ground up to handle complex tasks, like moving, populating, and integrating data from multiple sources. Meanwhile, they provide metadata and help businesses with data governance, supporting data quality even for novice teams.
With crucial functionality and standards that help reveal operational problems before they create bottlenecks in performance, ETL tools help increase resilience at the operational level. They also automate data flows, monitor them for issues, and alert IT teams when errors occur. The ETL process also eliminates the issues associated with hand-coded solutions, making for more efficient processing and reducing the chances of data integrity problems.
Track Lineage and Measure Impact
With deep insight into your data sets, ETL tools allow you to look more closely at reports, analyzing how every single result was calculated, along with where it came from and how recently it was updated. You can also review how data schema changes may alter your reports, and how to make the right adjustments.
Profile and Cleanse Data
Data-driven initiatives, including business intelligence (BI) and machine learning (ML), are only as strong as the data behind them. With ETL tools, you can better manage data by applying universal formatting and ensuring consistency amongst all data sets as you move and integrate information. This helps the end-users within your business find the most complete and relevant data for their needs.
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What Is Amazon Redshift?
Part of the larger cloud platform known as Amazon Web Services (AWS), Amazon Redshift is a managed cloud data warehouse service that operates at the petabyte-scale. As a business, you can use Redshift to store and analyze your data at-scale, keeping it in the cloud for secure storage and easy access from anywhere.
With a data warehouse service like Redshift, you can easily and automatically make sales forecasts and similar predictions. A service like Redshift also enables you to gain insights while benefiting from the latest in predictive analytics, informing your business's decisions and powering growth. However, the built-in ETL solutions for Redshift often get in the way of fully benefiting from a cloud data warehouse.
Using Amazon Redshift as Your ETL Solution
Redshift itself has extensive documentation regarding good ETL hygiene and steps to take to ensure your ETL processes perform optimally. If you're considering running ETL within your Redshift console, it's important that you understand what the maintenance will entail.
In its most basic form, using Redshift for ETL will require you to keep up with all of the following.
Evenly Divide Your Data
Since Redshift is a massive parallel processing (MPP) database, it takes data from multiple nodes at a time, allowing for many concurrent queries to run at once. At a glance, running a number of queries in concurrency may seem like it would greatly reduce execution time, but that's not necessarily the case. Your throughput will depend on how optimized your running queries are at any given time.
Clusters are divided into nodes, which are divided into slices. In Redshift, your runtime for any query will only ever be as fast as the slowest slice out of all of your node clusters. So, you must evenly distribute data across nodes and slices to allow for concurrency scaling, avoiding timeout, and keeping your ETL processes running efficiently in your default queue and beyond.
Of course, remember that a superuser is in charge of the config settings unless you assign the right access to another user group. If you're running a number of queries, make sure you check the parameter groups and adjust accordingly so you can achieve the concurrency level metric you want to see in your query queues. With a few steps, your WLM queries will run much more smoothly.
Routinely Maintain Tables
Redshift can rapidly create tables and rows. However, instead of actually deleting unused spaces, Redshift marks them for deletion. Over time, these old spaces can take up too much space inside of clusters and slow down runtimes.
To maintain your system tables, run functions like VACUUM and ANALYZE regularly. In doing so, you'll be able to identify and remove rows and tables that aren't being used.
Run Entire Transformations at Once
Most ETL transformations take several steps to complete, but every COMMIT is very resource-intensive. So, running steps individually will consume CPUs and increase runtime. To avoid this, use a bracket with a BEGIN...COMMIT statement, to run the entire transformation process as a single transaction.
Redshift supports massive data sets, so take advantage of that by loading your data in bulk. By doing so, you'll make the most of Redshift's unlimited scalability (backed by Amazon S3), making it simpler to copy large amounts of data into your Redshift console fast.
To do this correctly, before you copy large data sets into Redshift, accumulate all of it into an S3 bucket and use temporary staging tables to keep it there before transformation. This will allow you to perform a bulk copy operation for all the data that accumulates, at which point the temporary staging tables are dropped (but not deleted, so remember the above step).
Use Amazon Redshift Workload Management (WLM) for ETL
If you were going to try to build your own ETL solution for Amazon Redshift, you would likely end up using the Workload Management engine (WLM). Since Redshift itself is optimized primarily for read queries, Redshift suggests sending processes like ETL to a WLM queue.
Your WLM configuration will need to define a separate queue for your ETL process runtime. Generally, you will configure it to run using a slot count of up to five while claiming extra memory that particular queue has available, and taking advantage of the dynamic parameters for setting and controlling the amount of memory usage.
Separate query slots (or "query groups") make for fast queries, relatively speaking, but using the WLM engine has major downsides. Redshift's unique architecture makes it difficult for anyone to set up an efficient ETL process without intimate technical familiarity with Redshift's inner-workings. Understanding the downsides of Redshift's default ETL options is important before you decide how to proceed.
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The Downsides of Amazon Redshift ETL
While you can perform ETL within Redshift, there are downsides to doing so. The pain points we most often hear from clients include:
- Database queries are unacceptably slow.
- It's difficult to scale to meet increased demand.
- Integrating various sources can be a challenge, including SQL databases and SaaS products.
- Improper data architecture often leads to a tangled web of dependencies.
To solve the above issues, you'll need to gain a deep familiarity with Redshift or switch to a powerful ETL platform that can do the legwork for you, like Xplenty. As a data integration platform, Xplenty lets its customers to enjoy high-powered and effective Redshift ETL without the need to gain technical familiarity with Redshift.
How Does Offloading Redshift Data Work?
With the use of an ETL tool like Xplenty, you can do away with your Redshift ETL challenges and make your pain points disappear. With Xplenty, you can collect data straight from all of your sources and load it into Redshift or you can read the data from Redshift — no matter how it gets there — transform it and put it back in Redshift. Xplenty allows you to take either approach.
Your robust, Xplenty-powered ETL pipeline can solve the challenges of data integration. Plus, a custom-built ETL pipeline for Redshift that allows for elegant integrations is entirely possible, as long as you use the right tool. With Xplenty, you can use our simple drag-and-drop user interface and pre-built integrations to deal with I/O, connections, parallelism, and scheduling while maximizing your performance.
The following are the straightforward integration steps to connect Redshift with Xplenty and enable your business to make the most of Xplenty's ETL tools.
Loading Data From Redshift
You can load data from Redshift and process it with Xplenty by creating a Redshift connection and using this connection in a database source component. Xplenty reads data directly from Redshift just like all other databases, so the reading part isn’t currently offloaded while the processing is offloaded.
To integrate Redshift with Xplenty, you must first retrieve the relevant details about your Redshift cluster.
- Log in to AWS and go to the Redshift page.
- Go to the “Clusters” page and select the relevant cluster
- Keep a note of the endpoint, port, database name, username, and password. Note: the password is not available on this page.
Back in Xplenty, create the Redshift connection:
Log in to Xplenty.
Click on New Connection and select Amazon Redshift.
- Enter all the relevant connection details.
- Click Test connection to make sure that it works and then click Create connection.
Now you can create a dataflow that processes the data:
Go to the Packages page by clicking on the Packages icon in the left menu.
Open an existing package or create a new one.
- Click Add component at the top and select Amazon Redshift as the source.
Choose the Redshift connection that you have just created and fill in all the relevant fields. Please see the documentation for full details.
Click OK and continue editing your package.
Storing Results in Redshift
Xplenty can also store results back to Redshift once the batch processing is complete. The data is stored via S3, which is the fastest way to load data into Redshift.
- Add a new connection for the Redshift output, as described in the previous section.
- Open the relevant package and add an Amazon Redshift destination by clicking the plus sign below the last component.
- Choose the connection that you created and fill in all the relevant fields. Please see the documentation for full details.
You can use Redshift’s workload management to make sure that storing the data has a minimal effect on queries.
Easily Offload Your ETL From Redshift Using Xplenty
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You can easily offload your ETL from Redshift by integrating it with Xplenty. Xplenty can load data from Redshift, process it, and then store the results back to Redshift. This way you can keep your ETL and data warehouse at maximum warp speed and save plenty of time and money. Ready to get started? Contact our support team to find out how you can schedule a demo or get a 14-day pilot.