You acquired the skills necessary to maximize the cost of the application and infrastructure components in the earlier sections of this series. Accelerate Innovation by Shifting Left FinOps. This section will examine how to evaluate the design and minimize the cost of the parts that make up your data layer.
The methods for data cost optimization span a number of data domains, including but not restricted to Ingestion of:
- Data pipelines for data
- AI/ML
- Data Analysis
- Data Archiving, Retention, and Storage
This is another rapidly evolving layer of the architecture, and a small initial investment in the solution can result in significant cost savings.
Data Ingestion
Depending on the domain and the amount of data involved, this could be a resource-intensive, time-consuming, and expensive procedure. This stage involves the application pushing data to itself or the application pulling data from domain data sources. The choice of protocol may also have an effect on expenses. There may be fees involved if the push or pull is done through APIs, depending on how many times the API is called. Should you decide to use a file-based protocol, storage expenses will apply. The price may change based on whether the data is batched or streamed. The component that houses this data must be optimized for both storage and data input throughput.
Another crucial factor that must be agreed upon between the application and the data sources is the data format. Depending on the data type, there may be variations in the data intake times and, consequently, the cost. It is necessary to choose a data format that is both cost-effective and satisfies functional requirements. The cost of a number of additional elements, including processing, storage, and transfer, may also vary depending on the format. As an illustration
- Although CSV is a widely used and straightforward format, it is not cost-effective for storing data.
- Again, JSON is a text-based data format that offers greater flexibility than CSV. It does not, however, conserve space well, and data compression may not work well for this format, increasing the cost of storage and transmission, particularly for large datasets.
- Protobuf: Protobuf is a binary data format designed to be highly efficient and small. For complex schema high-performance computing applications, it can be a good option or format.
- Avro: Another binary data format that is efficient and small, Avro allows schema change.
- Columnar storage intended for usage with Hadoop and other downstream processing platforms, Parquet is a favored format that saves space and provides effective compression.
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Guidance
When compared to text-based formats like CSV and JSON, space-efficient and optimized data formats like Avro, Protobuf, and Parquet can help reduce storage and transport costs. These cost-effective data formats make it possible to store and analyze large amounts of data using less resources. Nevertheless, it is crucial to choose the appropriate format and protocol for data input based on additional considerations, such as client support and data availability for clients requiring downstream processing.
Data Pipelines
This layer’s services and components deal with data processing, which usually entails data enrichment and transformation.
Validating the receiving data and, if needed, converting it to a standard data format is the first step. Additional data manipulation techniques encompass schema matching, encoding, indexing, and data compression. The process of removing unnecessary data is another method that helps reduce expenses at this point. This will speed up processing and lower the cost of storage and transport. Data compression is done for the best possible storage and transport. This can be carried out as a component of data pipelines that use compression libraries and techniques such as gzip, Lempel-Ziv (LZ), etc., or as part of the data import stage.
Another significant cost to take into account is data access and transfer. Faster throughput from the application’s services and components translates to faster data access. In general, this lowers the solution’s cost as well. There are several methods for implementing quicker data access.
Frequently requested data can be stored in memory using a technique called caching, which eliminates the need for resource-intensive queries to the backend when accessing data. Although caching necessitates the addition of new solution components, it also lowers total costs by boosting throughput and lowering data access expenses.
Another tried-and-true method to improve query performance is indexing. Once more, while adding indexes to your data does have a cost, that cost is frequently outweighed by the increases in query performance and general efficiency.
In some circumstances, columnar storage—such as the parquet format—offers better query answers by facilitating quicker and more cost-effective data retrieval.
Schema design and data division are two other crucial components that need to be examined from a financial standpoint. Partitioning is breaking up large amounts of data into smaller, easier-to-manage chunks according to predetermined standards, such category or date. Data partitioning can save data access costs and improve query performance. To speed up data intake and retrieval, you can choose to create separate schemas for sub-domains or categories, or you can stick with a single monolithic schema, depending on the domain and kind of data.
The cost of processing data can be further decreased by utilizing serverless technologies like AWS Lambda or Azure Functions, which only charge for the resources that are really used and can be less expensive than dedicated servers.
To meet security needs and minimize costs for your use case, consider utilizing the network layer alternatives for data transfer, as discussed in the section above on networks.
Guidance
This step involves the key data pipeline activities of data access, transformation, and enrichment. One way to reduce the transformation cost is to adopt a canonical standard format for entering and exiting the application. You may increase productivity and minimize your cloud data intake costs without compromising data performance or quality by putting the aforementioned best practices into effect.
Data Analysis
This group of elements helps the solution side extract insights from the data it consumes. Selecting the appropriate service and from the appropriate source is crucial. Your product may become unprofitable due to the exponential increase in analysis costs. Therefore, it’s critical to choose the best service for our task.
For example, there are cloud service provider offerings like Redshift from AWS and GCP BigQuery from Google. Google BigQuery is a strong option for businesses that require dynamic data analytics because of its serverless architecture, which automatically expands to handle large quantities of queries. The cost of BigQuery is determined on the volume of data processed; a terabyte is $5. Large volumes of data can be stored and analyzed using Amazon Web Services’ (AWS) Redshift cloud-based data warehousing technology. The cost of Redshift is determined by how many computing nodes are used and how much data is stored. Compute nodes are priced at $0.25 per hour, and storage is starting at $0.023 per gigabyte per month.
Consider various third-party services like Databricks and Snowflake according to your use case and financial constraints. Built on top of Apache Spark, Databricks is a cloud-based platform for analytics and data engineering. The cost of a Databricks virtual machine (VM) starts at $0.15 per hour and is determined by the quantity of VMs utilized. The price of a Databricks cluster of 128 virtual machines (VMs) can range from $0.15 per hour to $3.20 per hour. Databricks pricing is depending on the number of VMs used per hour. Similarly, companies with unpredictable data analytics workloads should choose Snowflake, a cloud-based data warehousing technology that provides instantaneous elasticity and automatic scaling. Snowflake’s price is based on the volume of data handled and stored; monthly storage charges start at $23 per terabyte, while processing costs start at $2 per terabyte.
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Guidance
A crucial part of the answer is data analysis. To effectively meet your solution objectives and stay within your budget, you must use the appropriate cloud service for data analysis from cloud service providers or other third parties. Therefore, when comparing these platforms, keep in mind that each would provide a unique set of values and capabilities to the solution, offering varying degrees of flexibility at different pricing points. Given that Databricks price is dependent on the quantity of virtual machines (VMs) used each hour, it might be a more affordable option for workloads that don’t demand a lot of processing power. In the end, you have to decide which option best suits your unique data analytics requirements and objectives.
Data Storage, Retention and Archival
Another important part of your cloud bill is data storage, retention, and archiving; if not managed or chosen wisely up front, it can quickly spiral out of control. The access patterns have an impact on the cost as well. During the architecture/design phase, there should be a mechanism to assess the requirements on this front. Additionally, a method for periodically reviewing the guardrails and thresholds in light of business requirements should be in place. Data can be moved across tiers according to its age and consumption patterns by using datastores that offer automated lifecycle management. The objective is to obtain the most economical data recovery and storage while maintaining application availability and performance. Optimizing storage costs can be significantly impacted by parameters related to retention, archival, and time-to-live (TTL). Time-to-live (TTL) configurations specify the duration for which data should be stored in memory or on disk. Databases that enable this option move or purge data in accordance with the policy to minimize costs. In a similar vein, retention regulations outline the duration of data storage. You can guarantee that no data is kept longer than necessary by creating suitable retention regulations, which will save you money. Utilizing deduplication and compression also helps minimize the substantial amount of storage space needed for your data, which over time can result in significant cost savings. Data versioning raises the price of storage. Save data versioning for when it’s really necessary for the business.
Guidance: Accelerate Innovation by Shifting Left FinOps
It is necessary to set guardrails for particular data kinds and data repositories in accordance with corporate, legal, and regulatory constraints. To control costs, make use of lifecycle management and tweaking unique to cloud services. Retention, archival, and TTL settings can be managed with automation technologies, which simplify system optimization, lower the possibility of human error, and save costs. Automation and regulations can be set up well in advance of the software development lifecycle.
Summing it all
The goal of this series was to discuss the benefits and effects of moving left in finance operations for both the company and the clientele. In-depth discussions of numerous methods for cost optimization based on application design and every tier of your cloud-native architecture were also covered in the series. There’s a big chance to lower the cost of security, observability, and availability while also applying the same methodology to non-functional components. We will provide a worked-out example of applying these strategies, together with the associated savings and impact, in the final installment of this series.