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Published June 10, 2024

Streamlining Data Pipelines: Strategic Approaches to Cutting ETL Costs

Learn how Ocient significantly reduces the financial burden of extracting, transforming and loading data by 50% or more on your most compute-intensive workloads.

Ocient FaviconBy Ocient Staff

Large and complex datasets are the lifeblood of modern organizations, providing invaluable insights that fuel revenue growth in today’s competitive data-driven economy. Yet, aspirations for the analysis of big data are often hindered by the equally significant costs associated with its extraction, transformation, and loading processes — or ETL pipelines. That’s why understanding and reigning in these costs are so critical to stay ahead of the competition and ensure the longevity and value of data initiatives.

In this blog, let’s explore what contributes to ETL costs, what you can look for to optimize your ETL environment, and how innovative solutions like Ocient’s built-in ETL can significantly reduce the financial burden by 50% or more on your most compute-intensive workloads; amplifying operational efficiency and ultimately enabling innovation.

Common ETL Cost Contributors

Data integration via ETL processes is a substantial investment, with many cost drivers. In our daily conversations with customers, we often hear that their current infrastructure and tooling is too complex, data volume is too overwhelming, processes are too manual and the way they maintain and upkeep legacy tools is just too inefficient.

Let’s take a closer look at these four major cost contributors:

  • Infrastructure and tooling: From the hardware necessary to process, store, and move data, to the software and tools required for orchestration and ETL script development, infrastructure and tooling are foundational, but they’re often the most costly components of an ETL environment.
  • Data volume and complexity: Generally speaking, the more data you have, the more it costs to manage. But the complexity of the data can also drive costs up because of increased processing and management needs.
  • Human resources: Just like in any business, people’s time is money. Skilled data engineers and ETL developers are in high demand, and they come with a premium price tag. The human element of ETL processes is a continuous, recurring cost that cannot be overlooked.
  • Maintenance and upkeep: Over time, as your data ecosystem evolves, so must your ETL environment. Regular maintenance, updates, and system optimizations are necessary to keep your pipeline running smoothly, but they all come with additional expenses.

As you can see in the slide below from Intelligent Business Solution Analyst Mike Ferguson shows, the bottleneck around data ingestion and tool sprawl in these environments is a very real problem felt acutely across industries such as AdTech, telecommunications, and others that crunch very large datasets.

Simply put, more sources and more outputs add up to more costs for data teams being asked to do more with less.

Strategic ETL Cost Reduction Techniques 

And while no environment is ever perfect, nor is the work of a data team ever truly done, having a broad understanding of ETL cost components and the ideal ETL infrastructure is just the first step toward reducing costs. It’s also a good idea to review existing processes to identify ways to lower ETL.  

Data engineers can look at each of the following to review current pipelines: 

  • Optimize ETL job performance. By fine-tuning ETL job performance with techniques like data partitioning, indexing, and parallel processing, you can significantly reduce the time and resources required for each job, thus cutting costs. 
  • Data compression and encryption. Adopting efficient compression algorithms and handling data encryption in a smart, resourceful manner can reduce the volume of data handled and, therefore, the hardware requirements and associated costs. 
  • Cost-conscious development practices. Incorporate cost awareness into your development practices. Encourage your teams to consider cost implications when designing ETL processes, and make it a key performance indicator to optimize for low costs without compromising quality. 
  • Offload your largest datasets. Processing ultra-high-volume metrics or log data alongside many smaller fact or dimension table sources in a shared ETL environment can be a recipe for pipeline conflict, data delays, system strain, or even worse, data outages. Consider offloading your largest, most complex datasets onto systems that feature built-in data transformation during loading, thereby reducing the strain on the rest of your data pipeline. 

The Ocient Advantage: Redefining ETL for Cost Efficiency 

In the quest to reduce ETL costs, innovative solutions like Ocient’s integrated data transformation functionality can make a significant impact. It all starts with our unique loading capabilities and the unprecedented scale at which our platform can ingest data. When organizations move their large, complex, hyperscale data analytics workloads to Ocient, they benefit in two significant ways. First, Ocient makes their hyperscale workloads available for query much more quickly –in seconds to minutes versus hours to days. Second, they reduce reliance on standalone ETL tools like Informatica or custom pipelines in tools like Apache Spark. This, in turn, improves performance and simplifies pipeline administration across the business.  

With Ocient, organizations can easily stream data off Kafka or continuously load from file sources without doing any custom development. Ocient’s native data pipelines also seamlessly transform data during ingest and work with numerous source formats, data repositories and complex data types including JSON, arrays, matrices and geospatial data, as well as intra-database ELT capabilities. And, with an easy-to-use declarative SQL interface, Ocient expands the audience of users who can effectively transform streaming and batch data.  

Ocient’s Customer Solutions and Workload Services team can further improve ETL pipelines, leveraging its extensive experience to help customers re-architect their large-scale ETL environments with a focus on achieving remarkable efficiency. Drawing on years of hands-on involvement in data management and analytics, our experts understand the intricate balance between operational demands and cost constraints. By working closely with clients, they identify bottlenecks and inefficiencies in existing hyperscale data workflows and propose innovative, practical solutions. This consultative approach ensures that ETL processes are not only optimized for performance but also engineered to be cost-effective, enabling businesses to harness the full potential of their data infrastructures without excessive expenditure. 

For a more technical deep dive, watch the video below of Ocient Chief Architect George Kondiles at our whiteboard talking through loading and transformation. 

From Sprawling to Streamlined: The Ocient Team Delivers Results 

Ocient recently engaged with a customer looking to move a very large business-critical dataset to Ocient from four Netezza appliances and a legacy mainframe network. The data pipelines for this environment were highly complex, with considerable tool sprawl as the result of many years of growth and adaptation. The Ocient team coded, validated byte-by-byte, and developed new end-to-end procedures for the customer’s Netezza and mainframe systems, simplifying data migration with native support for complex data types including mainframe data. Ocient replatformed the entire solution in six months, replacing numerous extraneous ETL tools and delivering a full ROI in under 10 months. Ultimately cutting costs by $3.4 million over a 4-year period, streamlining ETL and developing an automated platform that will scale as the data grows, Ocient helped the customer reduce processing time by 96.3%.  

Extract More Value and Less Cost 

In the dynamic landscape of data management, controlling ETL costs is an ongoing challenge. By adopting a strategic approach, investing in the right technology and tools, and capitalizing on innovative platforms like Ocient’s, organizations can dramatically reduce the financial strains associated with data integration. 

Remember, the goal is not just to minimize costs but to do so without compromising the integrity, security, or performance of ETL processes. With careful planning and the right partnerships, an efficient ETL environment is well within reach, enabling you to unlock the full potential of your largest datasets while reducing costs across the board.

If you’d like to see a demo or if I can help answer your questions about our product, get in touch today.