What are some cost-efficient ways to optimize cloud data engineering?

Optimize, Innovate, Save! ???? I'd meticulously select the most efficient cloud services tailored to our specific data needs, optimizing storage and computational costs. Inspired by Martin Kleppmann's insights in "Designing Data-Intensive Applications," I'd refine data formats, automate pipelines, and scale resources smartly. I'd embrace continuous learning to keep abreast of cost-saving cloud data engineering practices, all while ensuring robust security and governance, thus maximizing value and minimizing expenditure.

To optimize cloud data engineering cost-effectively, leverage serverless architectures and auto-scaling features. For instance, use AWS Lambda for data processing, paying only for actual compute time. Employ managed services like Amazon Redshift for data warehousing, automating maintenance and scaling. Implement data compression and storage tiering in Amazon S3, reducing storage costs. Optimize query performance with partitioning and indexing. Additionally, use cloud cost management tools to monitor and adjust resources based on actual usage, avoiding unnecessary expenses. This approach ensures efficient utilization of resources while minimizing expenses, a key principle in effective cloud data workload optimization.

we can add this points Implement data compression techniques to reduce storage and processing costs. Use serverless computing to reduce costs and increase flexibility. Consider using open-source tools and platforms to avoid licensing fees. Optimize data ingestion by prioritizing critical data and setting appropriate frequency and batching. Use cost monitoring and management tools to identify areas where costs can be reduced. Implement data lifecycle management strategies to manage data as it ages and reduces in value. Utilize data caching to improve performance and reduce query time. Consider implementing a hybrid cloud approach to optimize costs and performance.

Below are few approaches:- * Right-sizing Instances like Analyze resource utilization and adjust instance sizes to best fit actual needs. * Leverage Spot Instances by utilizing cloud providers discounted instances with temporary availability for workloads tolerant of interruptions. * Optimize Storage by choosing the appropriate storage class based on access frequency and data type. * Automate Data Pipelines such as Schedule data ingestion, processing, and transformation tasks. * Compare Cloud Providers by analyzing the pricing models and offered services of different cloud platforms. * Implement Cloud-Native Design by Designing your data architecture with scalability and resource efficiency in mind.

Capability based planning and implementation is very important to realise business value and digital transformation. Choosing correct cloud provider is an important step, should be chosen keeping enterprise context in mind specially skill base within the company otherwise we run the risk of overspending, faulty implementation which causes bad use experience.

Incorrect usage of data storage not only create performance issues but also results in cost overruns. It’s important to keep the interoperability, information security, high availability and disaster recovery requirements in mind while selecting the correct data storage.

?? In case of data lake; combination of data table format and data file format can impact in how use storage and processing servers to support operations. ?? Check what features are supported all popular combinations of data table format (delta lake, Hudi, iceberg) with data file table (parquet, AVRO, OCR) and align with purpose of layer.

Data egress/ingress is one of the prominent line items in cloud infrastructure costs, so it is important to carefully architect the data storage and format based on the access pattern.

I would say the old but good transactional- and analytics-oriented mindset still applies when optimizing storage and formats. How many times and what for the data is accessed will define if highly compressed columnar data, derived data subproducts used during office hours or row-based in memory storage for transactional needs is preferred. Reinventing the wheel might not be needed as this challenge has been tackled for a while now and we can learn from the long history and product evolution of companies like Oracle, Google or even Cloudera.

Start with 3 Vs of data. Volume, velocity and variety. Then come up with the data strategy that's aligned with business strategy ( i.e. How data will be used to drive what business use case, internal, external, latency SLO, AI/ML/LLM etc.) Next step is enterprise level data architecture framework. Above 3 inputs will decide your data landscape. OLTP, OLAP, DW, Lakehouse, Separation of storage vs. compute, File formats in lake house, Data catalog, Data governance etc. Storage format is very subjective depending on the use case. For Lakehouse, iceberg parquet with zstd compression is a promising storage format.

Reliable and well-tested pipelines can reduce repeat processing due to errors and also provide for a way to monitor data usage. They also allow for abstraction layers which can help consolidating common data processing logic in order to reduce recurring costs.

Efficiently managing the computing resources so that they meet the requirements of most of our business users might be the best way to optimize the cost while providing maximum service quality. Define the data journey and quality (data contracts may be a good way to approach this), the product and SLAs to meet and automate this process. Most likely tweaking when and how data is consumed will render into the most economic approach. There are few things less harmfull than shadow IT and process duplication.

The key values of any pipeline stand for data, in many ways. Another way to say that may be: it needs to take on characteristics akin to data-quality metrics - such as reliability/consistency facing veracity from source formats, etc. Infra-reliability and the ability to stand up to audits - platforms provide these already, if configured well. There is almost nothing regarding data orchestration there isn't a few solutions for, already. Some may seem very complex, but the limitation on WebUI constructs currently actually leads to increased usability, especially when paired with GPT-esque assistance the platforms are starting to offer. The only blocker for most would be to understand the concepts & terminology - before embarking on.

It’s very important to provide right information to the right stakeholders at the right time and which can only be achieved using proper automation solutions. Data is generated from different sources so it’s import to aggregate, transform and load data to data ware housing , all of this is only possible using automation and data synchronisation.

Data pipelines will fail. Build it simple ( e.g. avoid transformation row-by-agonizing-row enroute), modular, self healing and observable by design.

I think a lot of teams miss updating their infrastructure over time. Updating instance types as new ones are released and keeping on top of savings plans or reservations are important over the lifetime of your service. These are as important as keeping the versions of your applications software up to date. We don't want to optimize infrastructure that is not current.

?? The auto-scaling feature let resource scaling in horizontal or vertical mode based in critios to do it. To use it, data engineering team must understand how business want to work, how face possible problems and what things prioritize feature in specific events to define these criterios. check what service can use this feature!

More than scaling, rightsizing is the way in which one can optimize the cost associated to company data needs. This often requires sensible resource tagging strategy so that cost and service can be monitored and, therefore, balance the service. Auto-scalling may overscale or underscale in exteme conditions so having a way to assess how good it is doing its job may also come from having both costs and service monitored efficiently.

Scaling Data Resources: Auto-Scaling Policies: Implement auto-scaling policies for your data processing clusters or resources. This ensures that your infrastructure automatically scales based on demand, optimizing resource utilization. Cloud providers often offer auto-scaling features for services like Apache Spark or Hadoop. Dynamic Resource Allocation: Utilize dynamic resource allocation mechanisms to adapt resource allocation based on workload requirements. Monitoring Data Resources: Performance Monitoring: Monitor the performance metrics of your data resources, including CPU utilization, memory usage, disk I/O, and network throughput. Latency Measurement: Measure the latency of your data processing tasks. Regularly review logs.

Due to the increasing focus and compliance needs, security and governance are considered a must in modern data-driven enterprises. A good data catalog or governance solution where metadata associated with the data journey and business processes is available allows for auditing as well as validating that company policies are enforced. Modern solutions do align this with company authentication and authorization mechanisms to produce a single point of information.

In addition to implementing security and governance best practices, here are further cost-efficient ways to optimize cloud data engineering: - Data Tiering and Lifecycle Management: Implement data tiering and lifecycle management policies to automatically move data to the most cost-effective storage tier based on access patterns and aging. This ensures that frequently accessed data resides in more expensive storage, while older or less frequently accessed data is stored in lower-cost options. - Query Performance Optimization: Optimize query performance by creating indexes, utilizing query caching, and employing query optimization techniques. This helps in reducing the amount of compute resources.

The access to all the data should follow well defined access patterns. Most of the cloud providers allow deep integration between various tools so that data can be accessed without duplication.

Security Best Practices: Encryption: Data Encryption in Transit: Ensure that data transferred between components is encrypted using secure protocols (e.g., TLS/SSL). This includes data flowing between cloud services, databases, and clients. Data Encryption at Rest: Implement encryption for data stored in cloud storage, databases, and other persistent storage solutions. Utilize native encryption features provided by the cloud provider. Access Controls: Implement robust access controls to restrict unauthorized access to your data resources. Use role-based access control (RBAC) and assign permissions based on the principle of least privilege. Regularly review and update access permissions to align with organizational changes.

Your data is going to grow as your business does. So make sure your ETL processes can handle the load. Make it happen by scaling it horizontally. Frameworks like distributed processing or cloud-based solutions can help you stay flexible. Businesses must implement performance-tuning practices such as index optimization, query optimization, and resource allocation adjustments. When you monitor performance metrics regularly and do continuous load testing. You can identify and resolve potential bottlenecks. We recommend you shift from performance testing to performance engineering to improve product quality and overall user experience.

Moving fast vs stable release. When building apps in cloud, it is mandatory for team to move fast and dedicate time to monitor and re configure architecture. This simple practice not only eliminate tech debts but also increases cost savings. In most cases, it is foremost important to atleast read through best practices of cloud service provider. Then adapt to that, and most cases optimises system for about 90% and once everything is settled down with app, a more complex approach to increase performance and reduce cost can be implemented.

Hackathons "face-to-face" are the most efficient way in my opinion, where we exchange techniques, personal perceptions, experiments, discovries, and even learn how (abd where) to learn

Here are additional cost-efficient ways to optimize cloud data engineering: - Serverless Computing for Ephemeral Workloads: For workloads that don't require continuous availability, consider serverless computing options to execute functions on-demand without the need to provision and manage servers continuously. - Data Deduplication and Compression: Implement data deduplication techniques to avoid redundant storage and reduce storage costs. Use compression algorithms for efficient storage and faster data transfer, especially for large datasets. - Cost-aware Data Transfer and Egress: Optimize data transfer costs by being mindful of egress charges when moving data between regions.

Adotar nuvem em modelo SaaS e PaaS costumam gerar algum nível de dependência, no entanto, costumam ser bem mais baratos do que IaaS e reduzem significativamente o overhead de manuten??o dos sistemas e servidores.

- Analyze and optimize your data pipelines to identify and eliminate bottlenecks. Utilize parallel processing and caching techniques to improve processing efficiency. - For large datasets, consider batch processing instead of real-time processing for cost-effective data transformation and analysis. - Automate routine tasks like resource provisioning, scaling, and monitoring to streamline operations and reduce manual effort.