{"id":32286,"date":"2026-10-08T14:51:44","date_gmt":"2026-10-08T09:21:44","guid":{"rendered":"https:\/\/opstree.com\/blog\/?p=32286"},"modified":"2026-10-08T14:51:44","modified_gmt":"2026-10-08T09:21:44","slug":"reduce-etl-pipeline-costs","status":"publish","type":"post","link":"https:\/\/opstree.com\/blog\/reduce-etl-pipeline-costs\/","title":{"rendered":"How To Reduce ETL Pipeline Costs Without Sacrificing Performance"},"content":{"rendered":"<p><span data-contrast=\"auto\"><a href=\"https:\/\/opstree.com\/blog\/optimizing-etl-processes\/\" target=\"_blank\" rel=\"noopener\">ETL pipelines<\/a> can be reliable without incurring excessive costs. As data volume grows, costs often rise not because of a single major error, but due to a series of small inefficiencies. These include refreshing entire tables, using excessively large compute resources, performing redundant transformations, unnecessary data transfers, wasteful storage usage and running pipelines more frequently than business needs require.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The challenge is that reducing costs should not simply mean slowing down the pipeline. A reporting pipeline that cuts costs but fails to meet the morning SLA (Service Level Agreement) is not truly optimized. Similarly, a fast pipeline that consumes excessive compute resources in the warehouse every night is not the right solution either.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The primary objective of optimizing ETL pipeline costs is to strike the right balance between cost, performance, data freshness and reliability. Current cloud best practices emphasize measuring the cost of each processing stage and aligning compute and storage resources with actual workload patterns.<\/span><\/p>\n<h2><span class=\"TextRun SCXW3875182 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW3875182 BCX0\" data-ccp-parastyle=\"heading 2\">Where ETL Pipeline Costs Usually Come From<\/span><\/span><span class=\"EOP Selected SCXW3875182 BCX0\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:299,&quot;335559739&quot;:299}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">Before changing the architecture, identify where the money is actually being spent.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">For most modern <a href=\"https:\/\/opstree.com\/blog\/enterprise-data-pipelines-fail-at-scale\/\" target=\"_blank\" rel=\"noopener\">data pipelines<\/a>, the primary costs are incurred in these areas:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li><b><span data-contrast=\"auto\">Compute: <\/span><\/b><span data-contrast=\"auto\">ETL jobs, Spark clusters, warehouse queries and transformation workloads<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Storage: <\/span><\/b><span data-contrast=\"auto\">Raw, staging, transformed and historical datasets<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Data movement: <\/span><\/b><span data-contrast=\"auto\">Transfers between systems, regions or <a href=\"https:\/\/opstree.com\/services\/cloud-migration-and-modernization-services\/\" target=\"_blank\" rel=\"noopener\">cloud services<\/a><\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Pipeline tooling: <\/span><\/b><span data-contrast=\"auto\">Connectors, orchestration and managed integration platforms<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Engineering effort: <\/span><\/b><span data-contrast=\"auto\">Monitoring, troubleshooting, failed runs and maintenance<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">There is another cost that often goes unnoticed: the expense of processing useless data.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Consider a scenario where 10 million records need to be reloaded, even though only 10,000 records have actually changed. This approach wastes computing resources and network bandwidth and consumes valuable processing time, without delivering any <\/span><span data-contrast=\"auto\">business value. One of the provided ETL benchmarks illustrates the significant difference between a full refresh and incremental processing.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This is where we should focus our efforts on optimization.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h3><span class=\"TextRun SCXW120084127 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW120084127 BCX0\" data-ccp-parastyle=\"heading 3\">Replace Full Loads <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW120084127 BCX0\" data-ccp-parastyle=\"heading 3\">With<\/span><span class=\"NormalTextRun SCXW120084127 BCX0\" data-ccp-parastyle=\"heading 3\"> Incremental Processing<\/span><\/span><\/h3>\n<p><span data-contrast=\"auto\">While refreshing data entirely is easy, ETL costs can rise as your data grows.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Instead of fetching the entire table every time, focus on processing only those records that have been added, updated or deleted since the last run.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Here are some common approaches to consider:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"auto\">Change Data Capture (CDC)<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Timestamp-based watermarks<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Change tracking<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Incremental merge or upsert strategies<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">Instead of processing the entire customer table repeatedly, the pipeline can use a &#8216;modified_date&#8217; watermark to focus on records that have changed since the last run.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">\u00a0The concept is very straightforward: Focus only on the changed data (delta) rather than the entire dataset.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This principle becomes even more critical for large enterprise databases, where tables may contain millions or billions of records, yet only a small fraction of them change between pipeline runs.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">With this in mind, Snowflake offers cost-optimization recommendations that facilitate incremental loading. This is particularly useful when datasets are so large that reloading them entirely would be costly or slow or would hinder the ability to keep data updated in a timely manner.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><strong><span class=\"TextRun SCXW101184566 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW101184566 BCX0\" data-ccp-parastyle=\"heading 3\">When should you keep a full load?<\/span><\/span><span class=\"EOP Selected SCXW101184566 BCX0\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:281,&quot;335559739&quot;:281}\">\u00a0<\/span><\/strong><\/p>\n<p><span data-contrast=\"auto\">Not every pipeline needs CDC, A full refresh can still make sense when:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"auto\">When working with small datasets<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">If it is not possible to reliably track changes from the source<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">When the entire dataset changes frequently<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">When rebuilding the target system is easier and less costly than maintaining incremental updates<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">It is important to note that the goal is not to completely eliminate &#8216;full loads,&#8217; but rather to avoid using them indiscriminately when they are no longer cost-effective.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h3><span class=\"TextRun SCXW189735021 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW189735021 BCX0\" data-ccp-parastyle=\"heading 3\">Right-Size Compute Instead of Simply Scaling It Up<\/span><\/span><span class=\"EOP Selected SCXW189735021 BCX0\" data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"auto\">When ETL pipelines begin to slow down, the standard approach is often to increase the size of the cluster or warehouse.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">\u00a0While this method can sometimes yield good results, it is important to understand that scaling up computing resources does not always guarantee better cost-performance.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">According to Snowflake, increasing the warehouse size does not always improve loading performance, particularly when the underlying issue relates to the number of files or their size.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Before making any changes to compute resources, consider the following:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"auto\">How much data are you actually processing?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Is the workload constrained by CPU, memory or I\/O?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Are jobs running even during downtime?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Are transformations scanning more data than necessary?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Is the warehouse optimized for peak demand, even though most operations are small-scale?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Can you implement autoscaling or job-based compute for your workloads?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span class=\"TextRun SCXW115658376 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW115658376 BCX0\">For scheduled ETL tasks, organizations can avoid infrastructure costs associated with idle time by <\/span><span class=\"NormalTextRun SCXW115658376 BCX0\">utilizing<\/span><span class=\"NormalTextRun SCXW115658376 BCX0\"> event-driven or job-based <\/span><span class=\"NormalTextRun SCXW115658376 BCX0\">compute<\/span><span class=\"NormalTextRun SCXW115658376 BCX0\">. AWS also recommends prioritizing compute and storage options based on actual workload patterns rather than treating capacity as a static requirement.<\/span><\/span><span class=\"EOP Selected SCXW115658376 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h3><span class=\"TextRun SCXW116277895 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW116277895 BCX0\" data-ccp-parastyle=\"heading 3\">Stop Repeating the Same Transformation<\/span><\/span><\/h3>\n<p><span data-contrast=\"auto\">Repeatedly using the same type of transformation logic often leads to a waste of computing power.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Consider this scenario: three different teams are working on building customer revenue models and all of them are using the same raw transaction data.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Even though the source data remains the same and the business logic is largely identical, the transformation process is carried out three times.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">A more effective approach would be to create reusable and standardized transformation layers that all teams can utilize.<\/span><\/p>\n<p><span data-contrast=\"auto\">There are two main benefits to this approach:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li><b><span data-contrast=\"auto\">Cost Savings:<\/span><\/b><span data-contrast=\"auto\"> We can reduce total costs by avoiding unnecessary processing.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Consistent Standards: <\/span><\/b><span data-contrast=\"auto\">Teams work according to uniform business definitions.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">This uniformity becomes even more critical in large enterprise environments, where data products, analytics teams, applications and AI workloads rely on the same underlying dataset.<\/span><\/p>\n<h3><span class=\"EOP Selected SCXW116277895 BCX0\" data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\"><span class=\"TextRun SCXW188273538 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW188273538 BCX0\" data-ccp-parastyle=\"heading 3\">Push Transformations to the Right Processing Layer<\/span><\/span><span class=\"EOP Selected SCXW188273538 BCX0\" data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/span><\/h3>\n<p><span data-contrast=\"auto\">ETL and ELT are distinct architectural strategies, not competing methods. In an ETL setup, data undergoes transformation before being loaded onto the target platform.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In contrast, the ELT approach involves loading raw data into the target system first and then transforming it using the computing power of the warehouse or lakehouse.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Do not optimize one stage while ignoring the total pipeline.<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h3><span class=\"TextRun SCXW61379303 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW61379303 BCX0\" data-ccp-parastyle=\"heading 3\">Reduce the Amount of Data You Process<\/span><\/span><\/h3>\n<p><span data-contrast=\"auto\">One of the simplest questions regarding cost optimization is:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Do we really need to process all this data?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Before your data enters expensive transformation or analytics systems, be sure to take the opportunity to reduce unnecessary data.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Here are some strategies that can be considered:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"auto\">Select only the necessary columns<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Filter out and remove irrelevant records as soon as possible<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Remove duplicate events<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Avoid reprocessing old data that has not changed<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Aggregate data when detailed records are no longer needed<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Distinguish between frequently used data and archived data<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">This is particularly important for large-scale event, log, and telemetry pipelines.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The sooner unnecessary data is discarded, the less effort downstream resources will need to expend on processing and storing it.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h3><span class=\"TextRun SCXW925987 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW925987 BCX0\" data-ccp-parastyle=\"heading 3\">Optimize<\/span><span class=\"NormalTextRun SCXW925987 BCX0\" data-ccp-parastyle=\"heading 3\"> File Formats, Partitioning and Storage<\/span><\/span><span class=\"EOP Selected SCXW925987 BCX0\" data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"auto\">Storage optimization is not just about reducing the volume of stored data. The way data is organized plays a crucial role in query performance and the efficiency of compute resources.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">For analytical workloads, using Parquet or other column-based formats reduces the amount of data that needs to be accessed. Additionally, partitioning can significantly minimize unnecessary scans, especially when queries frequently filter based on the partitioning key.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">AWS recommends using formats like Parquet or ORC for an optimal ETL process and emphasizes &#8216;partition pruning&#8217; as an effective way to limit the data required for processing.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">An effective storage strategy might include the following:<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Hot data \u2192<\/span><\/b><span data-contrast=\"auto\"> frequently accessed and performance-sensitive<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Warm data \u2192<\/span><\/b><span data-contrast=\"auto\"> occasionally accessed historical data<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Cold\/archive data \u2192<\/span><\/b><span data-contrast=\"auto\"> rarely accessed information retained for compliance or future analysis<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The objective is straightforward: to avoid incurring high processing or storage costs for data that typically does not require premium access.<\/span><\/p>\n<h3><span class=\"TextRun SCXW130063984 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW130063984 BCX0\" data-ccp-parastyle=\"heading 3\">Match Data Freshness to the Business Requirement<\/span><\/span><\/h3>\n<p><span data-contrast=\"auto\">Not all datasets require real-time processing.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This is a crucial decision involving costs, one that engineering teams often make without fully grasping the business context.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Consider the following points:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">\u201cHow fresh does this data actually need to be?\u201d<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Fraud detection systems require data to be updated within seconds. On the other hand, operational dashboards can function correctly even with a five-minute delay.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Similarly, financial reports generally need to be updated hourly or daily.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">If a five-minute latency is acceptable for business needs, processing data every few seconds can create unnecessary infrastructure and operational complexities without offering any real benefit.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Micro-batching can serve as an effective middle ground between traditional batch processing and continuous streaming.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Therefore, the best architecture should be based on &#8216;freshness SLAs&#8217;, rather than on the assumption that real-time solutions are always better.<\/span><\/p>\n<h3><span class=\"TextRun SCXW194378864 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW194378864 BCX0\" data-ccp-parastyle=\"heading 3\">Optimize<\/span><span class=\"NormalTextRun SCXW194378864 BCX0\" data-ccp-parastyle=\"heading 3\"> Data Loading Instead of Inserting Row by Row<\/span><\/span><\/h3>\n<p><span data-contrast=\"auto\">The loading strategy plays a crucial role in the performance of the <a href=\"https:\/\/opstree.com\/blog\/optimizing-etl-processes\/\">ETL process<\/a>.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">When data is inserted one row at a time, database operations and network latency increase as the volume of data grows.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">For large data loads, using staging files in conjunction with native bulk-loading techniques can significantly improve throughput. A benchmark test revealed a substantial difference in performance when comparing row-by-row insertion with the warehouse&#8217;s native bulk-loading methods.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Overall, the trend is as follows:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-32288\" src=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-staging-bulk-load-target-flow-1024x249.png\" alt=\"\" width=\"1024\" height=\"249\" srcset=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-staging-bulk-load-target-flow-1024x249.png 1024w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-staging-bulk-load-target-flow-300x73.png 300w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-staging-bulk-load-target-flow-768x186.png 768w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-staging-bulk-load-target-flow-1536x373.png 1536w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-staging-bulk-load-target-flow.png 1800w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p><span data-contrast=\"auto\">rather than:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-32289\" src=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-individual-insert-target-proper-1024x238.png\" alt=\"\" width=\"1024\" height=\"238\" srcset=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-individual-insert-target-proper-1024x238.png 1024w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-individual-insert-target-proper-300x70.png 300w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-individual-insert-target-proper-768x178.png 768w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-individual-insert-target-proper-1536x357.png 1536w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/source-individual-insert-target-proper.png 1800w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p><span data-contrast=\"auto\">How exactly this is implemented depends on the platform, but the principle broadly applies to modern data warehouses and databases.<\/span><\/p>\n<h3><span class=\"TextRun SCXW72699697 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW72699697 BCX0\" data-ccp-parastyle=\"heading 3\">Design Pipelines to Fail Cheaply<\/span><\/span><\/h3>\n<p><span data-contrast=\"auto\">A failure in the pipeline can significantly impact costs.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Suppose a pipeline consists of eight processing stages. If a malfunction occurs at the final stage and the entire workflow has to be restarted, the organization incurs additional costs to redo work that had already been successfully completed.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">By implementing a modular architecture, individual stages can be re-run without affecting the entire process.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Here are some useful approaches worth considering:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"auto\">Keep extraction, transformation, and loading stages separate<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Save intermediate results when necessary<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Use checkpoints<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Make pipeline operations idempotent<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Retry only failed operations<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Monitor data volume, latency, and failures for each stage<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">A reliable pipeline is not simply one that rarely malfunctions.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Rather, it is one that can be repaired quickly and correctly when a malfunction occurs.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h3><span class=\"EOP Selected SCXW72699697 BCX0\" data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\"><span class=\"TextRun SCXW183001691 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW183001691 BCX0\" data-ccp-parastyle=\"heading 3\">Make ETL Cost Visible to Engineering Teams<\/span><\/span><span class=\"EOP Selected SCXW183001691 BCX0\" data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/span><\/h3>\n<p><span data-contrast=\"auto\">To effectively optimize your <a href=\"https:\/\/opstree.com\/blog\/navigating-cloud-costs-the-power-of-aws-budget-service\/\" target=\"_blank\" rel=\"noopener\">cloud costs<\/a>, you need to have a clear understanding of where your spending is coming from. Instead of simply looking at your monthly cloud bill, consider breaking down your expenses into more specific categories:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"auto\">Pipeline<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Team<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Environment<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Data product<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Warehouse or cluster<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Query<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">Processing stage<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<h3><strong><span class=\"TextRun SCXW267211173 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW267211173 BCX0\" data-ccp-parastyle=\"heading 4\">Useful metrics include:<\/span><\/span><\/strong><\/h3>\n<div style=\"overflow-x: auto; width: 100%; margin: 25px 0; -webkit-overflow-scrolling: touch;\">\n<table style=\"width: 100%; min-width: 650px; border-collapse: collapse; font-family: Arial,Helvetica,sans-serif; font-size: 14px; line-height: 1.6; color: #374151;\">\n<thead>\n<tr style=\"background: #f5f7fa;\">\n<th style=\"border: 1px solid #ddd; padding: 12px; text-align: left;\">Metric<\/th>\n<th style=\"border: 1px solid #ddd; padding: 12px; text-align: left;\">Why it matters<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Pipeline runtime<\/td>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Shows performance changes<\/td>\n<\/tr>\n<tr style=\"background: #fafafa;\">\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Data processed<\/td>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Identifies unnecessary processing<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Compute usage<\/td>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Reveals expensive workloads<\/td>\n<\/tr>\n<tr style=\"background: #fafafa;\">\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Cost per pipeline run<\/td>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Makes optimization measurable<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Cost per GB processed<\/td>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Helps compare workloads<\/td>\n<\/tr>\n<tr style=\"background: #fafafa;\">\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Failure and rerun rate<\/td>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Exposes avoidable waste<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Data freshness<\/td>\n<td style=\"border: 1px solid #ddd; padding: 12px;\">Ensures cost reductions do not break SLAs<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><span class=\"TextRun SCXW57208304 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW57208304 BCX0\">This shifts the conversation from &#8220;Why is our cloud bill increasing?&#8221; to &#8220;Which pipeline is driving this increase, and why?&#8221;<\/span><\/span><span class=\"EOP Selected SCXW57208304 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2><span class=\"TextRun SCXW196653862 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW196653862 BCX0\" data-ccp-parastyle=\"heading 2\">A Practical ETL Cost Optimization Framework<\/span><\/span><\/h2>\n<p><span data-contrast=\"auto\">For an enterprise data platform, a useful optimization sequence is:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Measure \u2192 Identify \u2192 Reduce \u2192 Right-size \u2192 Automate \u2192 Monitor<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h4><b><span data-contrast=\"none\"> Measure<\/span><\/b><\/h4>\n<p><span data-contrast=\"auto\">Establish the current cost, runtime, data volume and freshness for important pipelines.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h4><b><span data-contrast=\"none\"> Identify<\/span><\/b><\/h4>\n<p><span data-contrast=\"auto\">Find full refreshes, expensive queries, duplicate transformations, idle compute and unnecessary data movement.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h4><b><span data-contrast=\"none\"> Reduce<\/span><\/b><\/h4>\n<p><span data-contrast=\"auto\">Move to incremental processing, filter unnecessary data and optimize storage and loading patterns.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h4><b><span data-contrast=\"none\"> Right-size<\/span><\/b><\/h4>\n<p><span data-contrast=\"auto\">Match compute capacity to actual workload requirements.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h4><b><span data-contrast=\"none\"> Automate<\/span><\/b><\/h4>\n<p><span data-contrast=\"auto\">Use scheduling, autoscaling, lifecycle policies, retries and cost alerts to prevent waste from returning.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h4><b><span data-contrast=\"none\"> Monitor<\/span><\/b><\/h4>\n<p><span data-contrast=\"auto\">Track both cost and performance continuously.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This approach avoids a common mistake: cutting infrastructure first and discovering later that the pipeline no longer meets the business SLA.<\/span><\/p>\n<h2><span class=\"EOP Selected SCXW196653862 BCX0\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:299,&quot;335559739&quot;:299}\"> <span class=\"TextRun SCXW178213422 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW178213422 BCX0\" data-ccp-parastyle=\"heading 2\">The Goal Is Cost-Efficient Data Engineering, Not Cheap Data Engineering<\/span><\/span><\/span><\/h2>\n<p><span data-contrast=\"auto\">Reducing ETL costs shouldn&#8217;t simply mean choosing the cheapest infrastructure every time. In reality, the goal should be cost-effective data engineering.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">A pipeline that saves money but delivers outdated data isn&#8217;t necessarily an improvement. Similarly, a fast pipeline that significantly drives up computing costs might not be the right choice for large companies in the long run.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The most effective architecture strikes a balance between the two:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span class=\"TextRun SCXW159131296 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW159131296 BCX0\">Cost + Performance + Reliability + Data Freshness + Scalability<\/span><\/span><span class=\"EOP Selected SCXW159131296 BCX0\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">That is why, instead of viewing ETL cost optimization merely as a one-time cloud cost assessment, it is crucial to adopt it as an ongoing engineering practice.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">For enterprise teams, the biggest benefits often come from simple changes: such as processing data incrementally rather than in bulk, eliminating unnecessary transformations, optimizing compute resources, improving storage and loading methods, and ensuring pipeline-level costs are clearly understood.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">When these strategic options are incorporated into the architecture, organizations can effectively scale their data platforms. This allows them to avoid the issue where infrastructure costs rise in direct proportion to the increase in data volume.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2><span data-ccp-props=\"{}\">Related Searches<\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/opstree.com\/blog\/data-engineering-companies\/\" target=\"_blank\" rel=\"noopener\">Top Data Engineering Companies in India In 2026<\/a><\/li>\n<li><a href=\"https:\/\/opstree.com\/blog\/enterprise-data-discovery-dpdp-readiness\/\" target=\"_blank\" rel=\"noopener\">Enterprise Data Discovery: Strategy, Tool Selection and DPDP Readiness<\/a><\/li>\n<li><a href=\"https:\/\/opstree.com\/blog\/agentic-ai-data-engineering-automate-etl-pipeline\/\" target=\"_blank\" rel=\"noopener\">What Is Agentic AI Data Engineering?<\/a><\/li>\n<li><a href=\"https:\/\/opstree.com\/blog\/complete-guide-to-data-pipelines\/\" target=\"_blank\" rel=\"noopener\">What Is Data Pipeline Architecture? A Complete Guide to Data Pipelines<\/a><\/li>\n<\/ul>\n<h2>Related Solutions<\/h2>\n<ul>\n<li><a href=\"https:\/\/opstree.com\/services\/generative-ai-solutions\/\" target=\"_blank\" rel=\"noopener\">GenAI consulting services<\/a><\/li>\n<li><a href=\"https:\/\/opstree.com\/aws-consulting-services\/\" target=\"_blank\" rel=\"noopener\">AWS migration partner for enterprises<\/a><\/li>\n<li><a href=\"https:\/\/buildpiper.io\/glossary\/ci-cd-pipeline\/\" target=\"_blank\" rel=\"noopener\">CI\/CD implementation services<\/a><\/li>\n<li><a href=\"https:\/\/buildpiper.io\/\" target=\"_blank\" rel=\"noopener\">enterprise software delivery platform<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>ETL pipelines can be reliable without incurring excessive costs. As data volume grows, costs often rise not because of a single major error, but due to a series of small inefficiencies. These include refreshing entire tables, using excessively large compute resources, performing redundant transformations, unnecessary data transfers, wasteful storage usage and running pipelines more frequently [&hellip;]<\/p>\n","protected":false},"author":244582689,"featured_media":32290,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[768739361],"tags":[768739758,768739756,768739754,768739755,768739759,768739757,768739753],"class_list":["post-32286","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-engineering","tag-cloud-etl-cost-optimization","tag-data-pipeline-cost-optimization","tag-etl-cost-optimization","tag-etl-performance-optimization","tag-etl-pipeline-performance","tag-reduce-data-engineering-costs","tag-reduce-etl-pipeline-costs"],"blocksy_meta":[],"jetpack_publicize_connections":[],"acf":[],"jetpack_featured_media_url":"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2026\/10\/How-to-Reduce-ETL-Costs.webp","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pfDBOm-8oK","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/32286","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/users\/244582689"}],"replies":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/comments?post=32286"}],"version-history":[{"count":2,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/32286\/revisions"}],"predecessor-version":[{"id":32291,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/32286\/revisions\/32291"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/media\/32290"}],"wp:attachment":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/media?parent=32286"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/categories?post=32286"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/tags?post=32286"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}