New Year = New Goals for Data Engineers: Cloud Cost Optimization and CDW Efficiency Guide for 2024

Introduction

With a new year comes an opportunity to set goals and resolutions to improve your cloud data warehouse (CDW). For many companies, a top priority is cloud cost optimization while also boosting efficiency. There are several ways to achieve these goals in 2024 without compromising your data infrastructure or overworking your data engineers.

The aim of this guide is to provide actionable tips to reduce your cloud data warehouse spend, streamline processes, and optimize your architecture. By following these best practices, you can cut costs substantially while speeding up queries and pipelines. This allows your data platform to scale efficiently while giving your data team peace of mind.

While initial cloud cost optimization may require some investment (Revefi's zero-touch copilot, it's less than 5 minutes), they quickly pay for themselves in savings and productivity boosts. A more efficient data warehouse allows data engineers to focus on delivering value rather than fighting fires. The steps outlined below work for both new and existing cloud data warehouses, helping you save money regardless of your current spend.

With some strategic planning and incremental improvements, you can cut cloud costs significantly while improving the efficiency of your data warehouse. Cloud cost optimization benefits your organization’s bottom line while allowing your data engineers to sleep better at night. Read on to get tips to optimize your architecture, queries, infrastructure, processes, and more. With the right resolutions, 2024 can be a very happy new year for your data team and company!

Assess Current Cloud Data Costs

The first step to cutting cloud costs for your data warehouse is to analyze the breakdown of your current spending. Login to your cloud provider's console and look at cost reports over the past 3-6 months. Assess the following:

• Which services are the biggest line items? Often the data warehouse, ETL processes, and BI tools are top spenders.
• Are you paying for more capacity than you need? Look at usage over time to decide what areas demand cloud cost optimization most.
• Are there unused additional services that can be removed, like extra database snapshots or backups?
• How much are you spending on ingress/egress data transfer? Large data imports can rack up costs.
• Look at allocated storage – is there unused space that can be reclaimed?
• Are you using the most cost-optimized instance types for each service? Right-sizing instances can save substantially.
• Are there workloads that could be switched to spot instances? Analytic or ETL workloads are good candidates.
• How often do you run ETL jobs, and how long do they take? Are they full or incremental? Are you at risk of overrunning your window? Running large transfer jobs too often is a sure way to drive up the bill. Assess what the business needs and adjust transfers appropriately.

Dig into the details and identify areas of waste or overprovisioning. Understanding where money is being spent is the first step toward purposeful cloud cost optimization. Target the biggest line items first for maximum impact. Maintain detailed logs and cost breakdowns going forward to continue monitoring expenditures.

Optimize Infrastructure

Rightsize instances based on actual usage to ensure you are running the most cost-efficient instance types. Many organizations overprovision compute capacity out of caution but end up paying for resources that go unused.

• Analyze CPU, memory, storage, and network usage over time to determine the lowest suitable instance type for each workload. 
• Leverage auto-scaling to dynamically adjust capacity during fluctuations in demand rather than running fully provisioned instances around the clock.
• Use spot or preemptible instances for experimental, temporary, delayable, or interruptible workloads to save up to 90% compared to on-demand. Monitor for early warning of interruptions to gracefully handle terminations.
• Shut down or suspend non-production environments like dev, test, and staging when not in use. Many organizations leave these running full-time without heavy utilization. Define startup and shutdown procedures to automate.

Right-sizing, auto-scaling, spot instances, and shutting down non-critical environments are key cloud cost optimization strategies that can yield dramatic savings. Continuously evaluate usage to ensure resources align closely with workload needs. Eliminating waste is the simplest way to cut cloud costs without reducing performance or capacity.

Implement Caching

Caching can provide significant cost savings for data warehouses by reducing the amount of processing required. There are a few key areas to implement caching:

Cache Query Results

Caching the results of expensive queries can avoid rerunning the full query each time the results are needed. This is especially helpful for queries that are run frequently with the same parameters. The cached results can be invalidated and refreshed when the underlying data changes.

Cache Intermediate Data

Many data warehouse jobs follow a workflow that transforms raw data incrementally through intermediate stages before ending up in the final tables. Caching intermediate stages can eliminate redundant processing when re-running portions of the workflow.

Cache Raw Data

Caching raw data that is used as inputs to multiple processing jobs is another approach to cloud cost optimization. Pulling the same raw data from object storage repeatedly can incur excessive bandwidth charges. Caching it after the initial load avoids these duplicate costs.

The key with caching is to understand your query patterns and data usage to determine which results, intermediate stages, and raw data inputs are used repeatedly. Caching these areas allows more efficient reuse while avoiding repetitive processing.

Tune Queries

Tuning SQL queries can often provide significant performance improvements and cost savings for cloud data warehouses. Here are some of the best cloud cost optimization practices you can adopt:

• Review execution plans. Analyze and explain plans to identify bottlenecks like full table scans or missing indexes. Target optimizations based on the most expensive operations.
• Add indexes. Properly indexed tables allow the optimizer to seek directly to relevant rows/blocks. Focus first on columns used for joins, aggregations, sorting, etc.
• Partition large tables. Breaking tables into partitions prunes data access during queries. Range/list partitioning on date columns works well for time series data.
• Use materialized views. Precompute aggregates, joins, etc., into materialized views. Query the materialized view rather than base tables to avoid expensive transforms.
• Collect statistics. Make sure table and column stats are accurate and up to date so the optimizer chooses optimal plans. Change monitoring can trigger re-collection.
• Parameterize queries. Use bind variables instead of literals in predicates and joins. Enables reuse of cached execution plans.
• Reduce data access. Only select columns needed, use row limiting, push predicates down, etc., to scan less data.
• Check data types. Ensure proper use of types like dates and timestamps. Avoid implicit conversions.
• Parallelize queries. Leverage MPP architecture by enabling parallel optimizers and scans to speed up long-running queries.
• Check your query timeout. Set it lower if it is too high to prevent expensive runaway queries.

With careful tuning guided by performance metrics and query plans, it's possible to achieve order-of-magnitude improvements in query times and reduction in compute resources. Invest time here for big savings.

Automate Processes

Automating key processes is important for cloud cost optimization and improving data warehouse efficiency. By setting up automation, you can minimize manual operations and ensure processes run reliably and predictably.

Automate ETL (Extract, Transform, Load)

Setting up automated ETL pipelines is crucial for keeping data up-to-date in your data warehouse. Instead of relying on engineers to manually pull data from sources, clean it, and load it, use a workflow orchestration tool to schedule and run ETL jobs. This saves significant time and effort while ensuring timely data availability. Popular ETL automation tools include Airflow, Azure Data Factory, AWS Glue, and Stitch.

For your convenience, we’ve covered the role of ETL and other must-haves of modern data stacks. Go check it to learn more.

Automate Reporting and Dashboards

Regular reporting and dashboards are essential business needs. Automating report generation and dashboard updates eliminates the need for manual refreshing. Set up scheduled jobs to run queries, generate reports, and update dashboards at a defined cadence (hourly, daily, etc.). Look for reporting solutions with API access that enable automation and foster cloud cost optimization.

Automate Infrastructure Scaling

As data volumes and workloads fluctuate, automating the scaling of cloud infrastructure is key to optimizing costs. Use auto-scaling groups to dynamically add or remove capacity based on metrics like CPU usage. Set policies to scale down during quiet periods to minimize waste. Cloud providers like AWS and Azure offer auto-scaling capabilities.

Automate Maintenance Tasks

Handle data warehouse maintenance like vacuuming, analyzing statistics, and cleaning up old data through scheduled automation. This ensures regular care without tying up engineers. Make sure automation runs during off-peak hours to minimize performance impact.

Automating key processes reduces manual burdens while improving reliability and cost efficiency for cloud data warehouses. The time investment in automation pays off greatly over the long term.

Choose Cost-Efficient Services

When it comes to cloud services, you often have the choice between managed services provided by your cloud vendor or self-managed open-source alternatives. Managed services typically come with a higher price tag but save you engineering time and effort. Self-managed open-source software gives you more customization and control but requires maintenance and operations.

For non-critical workloads, opt for managed services with lower operational costs over self-managed. Prioritize spending engineering time on core workloads and using managed services for non-core needs.

Review all services powering your data warehouse end-to-end and determine if managed services could reduce your operational overhead. The savings on engineering costs often outweigh the higher service fees, making them powerful cloud cost optimization drivers.

Monitor and Alert

Monitoring your cloud costs and system performance is critical for controlling your cloud spend. Here are some tips:

• Track spend and resource usage. Monitor your spending and resource utilization over time. Watch for unusual spikes or trends.
• Monitor query performance. Track query execution times, queue lengths, and throttle rates to identify inefficient queries. Alert when queries exceed thresholds.
• Get alerted on anomalies. Set up monitors to notify you when usage, costs, or system metrics exceed the boundaries defined in your cloud cost optimization checklist. React quickly to anomalies.
• Log activity. Capture log data on user activity, query execution, data access, etc. Analyze logs to identify optimization opportunities.
• Use tagging. Tag resources by the owner, project, and environment to identify what is using capacity and dollars. Get visibility at a granular level.
• Schedule reports. Have automated reports on spend, usage, and performance sent to stakeholders on a regular basis. Make sure cloud costs don't go unnoticed.

Continuously monitoring your cloud usage and spend is essential for maximizing value. The right visibility and alerts can help you stay within budget and avoid unexpected costs.

Architecture Improvements

Improving your overall data architecture can lead to significant cloud cost optimization and performance gains in your data warehouse. Here are some key areas to focus on:

• Transition to a microservices architecture. Break up your data pipeline into smaller, loosely coupled services. This makes it easier to scale and optimize each component. For example, you can have separate microservices for ETL, analytics, APIs, etc.
• Implement a data lake. Use a data lake for raw storage and access. This is much cheaper than storing all transformed data in your warehouse. Use your data warehouse for curated, analysis-ready data.
• Adopt a data mesh architecture. Decentralize data products and domains across autonomous teams. This avoids central bottlenecks and allows teams to manage costs and performance for their domain. Implement common standards and discoverability.
• Optimize query paths. Structure your data so queries don't have to scan unnecessary files and partitions. Prune older data to cheaper storage. Organize tables and partitions to optimize query performance.
• Right-size cluster nodes. Choose instance types optimized for your specific workloads. Shut down unused nodes. Use auto-scaling to match demand.
• Implement caching. Cache commonly queried results and intermediate steps. This reduces the load on your cluster and improves response times.

With a scalable, cost-efficient architecture, you can meet growing analytics needs while actually lowering your data warehouse costs. Careful architectural decisions will pay dividends.

Conclusion

As we come to the end of this article, let's recap the key steps we covered to help you achieve cloud cost optimization and improve data warehouse efficiency so that your data engineers can get more sleep:

• Assess your current cloud costs and usage to identify areas to optimize – look at underutilized resources, overprovisioning, and wasted spend. Create visibility into what services and resources are driving costs.
• Optimize your infrastructure by right-sizing instances, shutting down or suspending unused ones, and choosing cost-optimized instance types and families. Take advantage of autoscaling, serverless options, and spot instances.
• Implement caching mechanisms like Redis or Memcached to reduce the load on your warehouse and make queries faster. Cache expensive computations or queries that are run repeatedly.
• Tune your queries to run more efficiently, keeping cloud cost optimization in mind – avoid expensive queries, introduce indexes, partition your data, and optimize table design. Refactor code to reduce processing needed.
• Automate manual processes for extracting, transforming, and loading data to reduce engineering time spent. Automate monitoring, scaling, and scheduling where possible.
• Choose the most cost-efficient cloud services for your workloads, for example, object storage over block storage. Leverage tools like Athena to query data directly.
• Continuously monitor usage and costs to identify optimization opportunities. Set up alerts for spikes or thresholds. Track metrics over time.
• Make incremental architecture improvements like introducing a data lake, pre-processing layer, or query federation. Move cold data to cheaper storage.

By following these steps, you can significantly cut cloud costs, speed up your data warehouse, reduce your engineering workload, and ultimately enable your data team to sleep better at night! The savings unlocked can be invested in creating more value for your organization.

Maximize CDW Cost Management Efficiency with Revefi’s Data Operations Cloud

Revefi ensures seamless automated data observability for enterprise-level CDWs to unburden your data engineers and maximize the productivity of your data stack while keeping warehouse spending low.

Setting up your data quality monitorings with Revefi requires no coding: Revefi deploys them automatically. Once the platform scans your metadata, you will get automated notifications on excessive CDW use and anomalous data right away.

Get started today – try Revefi for free to see how simple data observability can be.