There has never been a better time to start a startup, thanks to all of the advancements in communications and data management technology. Data engineering and utilization are at the core of every new startup that plans on disrupting and dominating its markets. Fortunately, Amazon Redshift can make the data management aspects of running a business much easier. Here are 11 Amazon Redshift tips for startups.

Table of Contents

  1. Optimize Big Data Management with Amazon Redshift Advisor
  2. Save Time with Massively Parallel Processing
  3. Sustain High Performance with Periodic Maintenance
  4. Take Advantage of Scalable Pricing
  5. Build a Strong Foundation with Amazon AWS
  6. Automate Data Management Workflows with Amazon WLM
  7. Get IT Infrastructure Resources From Redshift
  8. Adopt Redshift Quickly Because of SQL
  9. Perform Detailed Analyses with Amazon Redshift Spectrum
  10. Amazon Redshift is Available Around the World
  11. Integrate Redshift with Other Services

1 Optimize Big Data Management with Amazon Redshift Advisor

Startups need mentors for learning to run a business, finding ways of reaching customers, and developing sales systems. Amazon Redshift Advisor can be your mentor for optimizing your Redshift implementation.

Amazon Redshift Advisor is a machine learning system that makes recommendations on how to optimize Redshift.

Combined with Amazon’s support infrastructure, creating your IT infrastructure based around Redshift is attainable for any size startup.

2 Save Time with Massively Parallel Processing

Startups gain an advantage in their niche if they can aggregate and process data sets efficiently. The more data you can aggregate and process, the better your insights into the market. Supercharge your processing power by leveraging Redshift’s massively parallel processing (MPP).

Redshift’s resources and optimized code make it possible to run multiple processes at the same time. It leads to exponential growth in processing capability by reducing the latency when completing workflows. 

Your staff spends more time going over the results of analytical queries rather than stressing over query performance or designing schemas. For a startup, faster processing is a major business advantage.

3 Sustain High Performance with Periodic Maintenance

Like any data warehousing system, your Amazon Redshift data implementation needs periodic maintenance to sustain its high query execution performance, specifically, the databases that you feed to it.

When working with Redshift, repair and defragment tables often, data files can fragment over time and make it difficult to aggregate data. This is especially true if your company removes data from the relational database, adds new resources like Amazon S3, and restructures data often. 

This process uses any memory available in real-time, even if the cells are not next to each other. Data files can be split between cells on opposite sides of the memory media, making fragmented schemas for data files. This also reduces throughput as loading data is more complicated.

Establish a maintenance schedule for your databases when you know you will have downtime or slower productivity times. Doing so can minimize the impact on your operations.

4 Take Advantage of Scalable Pricing

Startups are often limited when it comes to cash and paying for resources. Amazon Redshift uses a scalable pricing structure so that you only pay for what you use. Redshift charges by the hour for the resources that you use. If you need to resize up, your costs will increase. 

The pricing structure also provides predictability. If you know that your data usage will spike next month, you can easily calculate your costs. You are not tied into a contract with specific usage rates, so you can adjust your usage to limit your costs as you need to in different use cases. 

5 Build a Strong Foundation with Amazon AWS

Amazon Web Services (Amazon AWS) is a data warehousing system for columnar databases that is also cloud-based. It can provide features like massively parallel processing for database management needs, which allows the system to capitalize concurrency and run processes in parallel.

Startups steer clear of these systems because they seem too complex to manage. In reality, AWS services should be used by businesses of any size because it is scalable to the size of that business. 

For example, finding a reliable cloud storage system can be a challenge for startups. Amazon S3 works with Redshift and other AWS resources, making it possible to have an entire IT infrastructure based on AWS.

Building your startup’s big data management infrastructure on Amazon AWS and Amazon S3 ensures that you can use this one system forever, which is more cost-effective. Popular startups, like Lyft, AirBNB, Stripe, and Slack to name a few, started by using Amazon AWS powered by Redshift. Not only did these companies scale effectively, but their Redshift implementations also maintained their high-performance speeds thanks to the concurrency in processing.

6 Automate Data Management Workflows with Amazon WLM

Getting a startup off the ground can hinge on establishing workflows that leverage data science to make workflows that make staff more productive. You can improve performance metrics by automating your workflows in Redshift using Amazon Workload Management (WLM).

WLM lets you compile workflows for data mining and from various data sources. You can even set priorities so that the system knows which tasks to focus more effort on and which tasks can benefit from concurrent processing.

7 Get IT Infrastructure Resources From Redshift

Technology is essential for startups to function these days. Rather than spending a lot of money on your IT infrastructure, use an Amazon Redshift cluster. Amazon handles all of the back-end services, like AWS, which is a cloud-based data management system. The system scales to meet your data warehousing requirements for different data types as well. 

You never have to worry about adding more compute nodes yourself. For a startup, Amazon gives you a lot of computing power to run your business on, with a potentially infinite pool of resources to add to your Amazon Redshift cluster as you need them. 

8 Adopt Redshift Quickly Because of SQL

Adopting Amazon Redshift doesn’t require extensive training or tutorials in new technologies. It is powered by industry-standard SQL and works with a variety of SQL alternatives, such as PostgreSQL or MySQL. 

For example, standardized SQL makes it possible for systems that are based on SQL to communicate. Regardless of which version you use, components like sort keys are always the same. This makes it possible to customer the infrastructure that you use without having to rebuild or relearn different systems.

There are many systems that support SQL that can also work alongside Redshift. As long as there is compatibility, like using distribution keys to sort data, for example, Redshift can work with nearly any other ETL system.

Amazon Redshift can even work with some systems that do not use SQL. DynamoDB is a leading NoSQL alternative managed by Amazon and can be just as effective at working with Redshift. 

Faster adoption of the platform also means faster product production. Fewer compatibility problems, like matching sort keys to their analog in other programs, companies can focus on getting the work done on their offerings. 

9 Perform Detailed Analyses with Amazon Redshift Spectrum

Amazon Redshift Spectrum is the data analysis platform within Redshift. It gives data analysts access to a wide variety of analytical tools that work with a large amount of data. They can perform analyses on data in AWS without having to extract it and move it to a different system. 

Having a set of data modeling tools built into the platform makes it much more efficient to perform data analytics. Startups can find many of the data analysis and ETL tools that they need inside the AWS platform. However, outside tools can also integrate with Redshift Spectrum for added functionality. 

10 Amazon Redshift is Available Around the World

Although starting small, startups can grow rapidly thanks to global exposure. Amazon Redshift operates on and is available to a global market, making it perfect for startups.

Amazon turned Redshift into a global resource for exactly that reason. Now that anyone can gain access to it, startups are no longer limited by the resources in their home country. 

This is also better for startups with an international work team or customer base. No matter where they are, your people have access to a powerful platform to manage data lakes and base operations on. 

11 Integrate Redshift with Other Services

Data helps startups make rapid developments, reach customers, and grow exponentially. However, collecting data is not enough. They need to use ETL processes to put that data to use. Amazon Redshift provides the data warehouse that your startup needs for this, but an ETL tool is also needed. Maximize the use of your data by using is a cloud-based platform for ETL processes. It provides the tools needed to visual data pipelines and creates automated workflows that turn raw data into refined business intelligence. To learn more about how can help get your startup off the ground, schedule a 7-day demo or discuss your needs with one of our implementation consultants.