The economic environment today is quite different from the low interest rate years of the last few years. All economic indicators seem to be showing clear signs of slowdown across all sectors of the economy. As of this writing, it seems like we’re in for a few quarters where both governments as well as organizations would focus on rationalizing costs.
Data teams are no exception to this trend. Everyone is convinced about the value of data today, however, there has been an almost unchecked rise in consumption of data related tools and services leading to runaway costs in organizations. Cloud data warehouses, modern ELT tooling and the increasing number & volume of data tools that organizations have acquired today adds to this problem. This was fine in better economic times, but not today.
Against this backdrop, we’re quite certain that the demands of business users are only going to increase from here on in. The march towards the modern data stack is inevitable, as more and more of us discover the value of building truly scalable and resilient data technology stacks. How does one find a balance? In this article, we explore some effective strategies for D&A (data and analytics) teams to explore for cost optimization without sacrificing performance.
#1 Embracing open-source technology:
When anyone asks me about the #1 thing data teams can do today to rationalize costs, my answer is: Open-source. Also, when someone asks me the #1 thing that data teams can do to drive innovation: Open-source. This is really a no brainer which provides a myriad of benefits. Open-source tools can offer cost-effective alternatives to licensed software - a few examples are meltano for data extraction and ops, dbt core for data transformation, and Apache Superset for data visualization. By leveraging open-source solutions, your data team can access powerful tools without breaking the bank. Plus, you'll be connected to vibrant developer communities that can offer valuable support and insights. These communities are constantly developing open-source tools to compete with commercial software and even lead the way when it comes to innovation.
#2 Standardizing data pipeline processes and technology stack:
Implementing standardization in your data pipeline can enhance efficiency and collaboration among teams. The first step to doing so would be to build a data stack that's taking care of multiple user requirements. Standardizing on which tools & processes to use will go a long way in rationalizing costs. In addition, adopting agile project management practices, making sure all your data engineering code is synced in code repositories, and maintaining thorough technical documentation are all essential steps in creating a more unified approach. Standardization not only helps streamline processes but also promotes a consistent experience for new team members. By ensuring everyone is on the same page, your organization can maximize the benefits of the modern data stack, resulting in better cost management.
#3 Considering local machines for workloads:
Yes, this is definitely a contrarian take - but allow me to explain why. The advancements in laptop processing power, such as Apple's M1 and M2 chipsets and the latest from Intel and NVIDIA, make it possible to shift some workloads back to local machines. For example, the Apple M series chipsets are already well known for being able to comfortably handle workloads for data & analytics. This, in combination with new-age OLAP solutions like DuckDB, your team can reduce cloud-related costs and make your team more efficient. Evaluating which tasks can be shifted back to local machines can result in significant cost savings and more efficient resource utilization.
#4 Implementing robust cost monitoring and accountability:
Establishing a system for tracking and monitoring data-related costs is crucial for effective cost management. By implementing regular cost monitoring, using tools like cloud cost management platforms or custom monitoring solutions, and assigning responsibility for cost management within the data team, your organization can ensure accountability and avoid unexpected expenses. Staying vigilant about cost trends can help your data team make more informed decisions and better align with modern data stack practices.
#5 Identifying and eliminating dark data:
Unutilized data hiding in your storage systems can lead to unnecessary storage and processing costs. Conducting an audit to identify and eliminate dark data can free up valuable resources and help your data team operate more efficiently. Streamlining your data storage can contribute to overall cost optimization and allow your team to focus on extracting valuable insights from relevant data. One of the other very effective ways to drive down the prevalence of dark data is to implement a state of the art data cataloging solution in your organization - there are brilliant open-source solutions that address this too!
By incorporating these strategies and staying committed to the principles of the modern data stack, your data team can optimize costs without sacrificing performance. Balancing cost management with the ever-evolving needs of your organization is essential for success in today's economic landscape.
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