All organizations, big or small, are struggling to manage the data they have and drive actionable insights. Data analytics help them make informed decisions that can eventually assist them in staying ahead of their competitors.
What are the challenges with data pipelines?
- Slow data pipelines are challenging even for organizations with savvy data scientists.
- Smaller organizations are operating without relevant tools and relying solely on manual processes.
- Engineering effort is huge.
- It would help if you had a team of tech-savvy people to get started.
So is there a solution to build your data analytics stack?
Building a data analytics stack for your business data in Amazon Redshift
Yes, there is a solution where you can utilize tools like Amazon Redshift and ETL to create a data analytics stack for your business data.
What is Amazon Redshift?
Amazon Redshift is a petabyte-scale, massively parallel data warehouse. It is a fully managed with virtually unlimited storage and computing power, thanks to the cloud, and that too at an affordable rate starting at a mere 0.25$/hour.
It wouldn’t be wrong to say that Amazon Redshift has made secure data storage simpler, more efficient, and a whole lot cheaper.
With Amazon Redshift, storing, managing, and maintaining data is no longer one of the many concerns of modern businesses; AWS offers reliability, durability, and security for your data. Read more here.
Setting Up Your Amazon Redshift ETL Data Pipelines
Amazon Redshift’s popularity and widespread adoption among seasoned data scientists and growth hackers alike stem from its usability and easy integration with all of the major languages. After setting up your Redshift instance, the next step is to populate your data warehouse with consolidated data from disparate data sources.
The best way to do that is by utilizing AWS ETL tools that can collect data from all customer touchpoints and load that into Amazon Redshift with minimal effort.
Although it is an ELT tool (ETL vs ELT), it performs some transformation during loading to reduce the time between extracting data from multiple sources and gaining data-driven insights by a whopping 90%. Such AWS ETL tools can enable you to set up a data pipeline within minutes and easily manage it from then onwards.
ETL for Amazon Redshift towards Faster and Smarter Analytics
Building your data pipelines to Amazon Redshift with an ETL service can take care of managing the best practices for optimal performance by leveraging parallelism, efficient data storage, and optimized column compression.
Nonetheless, after setting up data pipelines to feed your Business Intelligence and custom analytics tools, you still need to perform periodic maintenance on your Amazon Redshift instance.
Otherwise, you’ll soon start experiencing compromised query performance. Regular maintenance and focusing on table hygiene will keep your Redshift cluster in shape.
It’s important to remember that deleting data using the DELETE command will only mark the row without really deleting it. Over time, rows marked for deletion can accumulate. To free the disk space occupied by these deleted rows, you need to use the VACUUM command at regular intervals.
Amazon Redshift is a columnar database; and although by default, it stores data in uncompressed form, it is possible to apply column compression encodings to reduce column size which results in less disk I/O operations and better query performance.
Choosing the right compression encodings depends on the data as well as the analysis that is to be performed on it. As data and analytics evolve, you will need to re-evaluate your compression encodings.
Keeping an eye on the performance and query execution time is crucial for ensuring that everything is functioning optimally.
Monitoring query performance is an integral part of Amazon Redshift cluster maintenance. You can see how queries are performing on each cluster through the AWS Console. You can also get an insight into CPU utilization and network throughput against each query.
Apart from an overall view of queries and their performance, Amazon Redshift allows you to dig deeper into the system information and performance stats as well. Through monitoring, you can not only identify and issues at an initial stage, but you can also understand how queries perform and optimize accordingly.
Data Sources for Amazon Redshift
Data sources API types are essential for your ETL. Some data sources and APIs are difficult to handle and work. Others need a lot of maintenance and a lot of back and forth with the people that will use your data.
For example, to ETL Salesforce to Redshift needs connecting with an API that needs much work. An ELT service like Blendo can connect there in minutes and give you analytics-ready data in your redshift data warehouse in the first minutes.
Other data sources are notoriously difficult to work. Facebook Ads API needs someone from your team to be on top and maintaining it often. An ELT service like Blendo can connect in Facebook Ads faster and give you analytics-ready data that you need.
An Amazon Redshift data warehouse is an excellent way of having data from all sources consolidated and available in an analytics-ready form.
Feeding this data into your BI tools and more sophisticated predictive analytics tools can equip you with all the information that you need to make effective business decisions. With the right ETL service like Blendo that is better suited for leveraging cloud scalability, creating and managing business-ready data pipelines in Amazon Redshift becomes even more convenient and rewarding.
By utilizing such tools and technologies, and keeping in mind the tips mentioned above, you can build a data analytics stack that can take your business to the next level.
Blendo is a modern ETL platform (ELT) designed to offer self-serve analytics-ready data in modern data warehouses. Don’t hesitate to contact us and try Blendo for free.