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This newsletter aims to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll share articles from various sources I found interesting. The following 5 articles/videos made the cut for today’s newsletter.

1. Data Contracts 101 by Aurimas Griciūnas

The concept of a Data Contract is an agreement between Data Producers and Data Consumers on the schema, SLAs, semantics, lineage, and other details of the data being produced. Data Contracts should be enforced to ensure data quality, prevent unexpected outages, enforce ownership of data, improve scalability, and reduce the intermediate data handover layer. An example implementation of Data Contract Enforcement involves schema changes in a git repository, data validation against schemas in the Schema Registry, pushing validated data to a Validated Data Topic, validating data against additional SLAs, and alerting Producers and Consumers to any SLA breaches. Read more here

2. A brief history of Data at Coinbase by Michael Li

The article provides a brief history of data and its importance in the development of Coinbase, a cryptocurrency exchange platform. The author explains how the concept of data has evolved over time and how Coinbase has utilized data-driven decision-making to improve its platform and expand its user base. The article also discusses the potential of Web3, a new decentralized web infrastructure, and how it can revolutionize the way data is stored, shared, and used. The author concludes by emphasizing the importance of data in the growth of Coinbase and the potential of Web3 to transform the future of data.
Read more here

3. How to use the Snowflake Query Profile by Ian Whitestone

The article explains how to use the Snowflake Query Profile, a feature of the Snowflake cloud data platform, to diagnose and optimize SQL queries. The Query Profile provides detailed information about the query execution plan, including the amount of time spent on each operation, the number of rows processed, and the resources consumed. The article walks through the steps of running a query and analyzing the Query Profile to identify potential bottlenecks or areas for improvement. The author also provides tips for optimizing queries based on the information provided by the Query Profile. Overall, the article offers a useful guide for developers and data analysts looking to improve the performance of their Snowflake SQL queries. Read more here

4. Building Modern Data Teams by Pedram Navid:

The article discusses the characteristics of modern data teams and the key roles involved in building and managing data infrastructure. The author argues that data teams should be cross-functional, collaborative, and focused on delivering business value through data insights. The article identifies several key roles in modern data teams, including data engineers, data analysts, data scientists, and product managers. The author provides an overview of the responsibilities and skills required for each role and emphasizes the importance of communication and collaboration between team members. The article also highlights some of the challenges faced by data teams, such as data quality and security, and provides tips for overcoming these challenges. Overall, the article provides a useful perspective on the structure and function of modern data teams. Read more here

5. How to Prioritize Analytical Work by Elvis Dieguez

The article provides tips on how to prioritize analytical work effectively. The author suggests that prioritization should be based on the business impact of the analytical work, as well as it’s level of complexity and the urgency of the request. The article recommends creating a prioritization matrix that takes these factors into account and prioritizing work based on its placement in the matrix. The author also emphasizes the importance of communication and collaboration with stakeholders to ensure that priorities are aligned with business needs. Additionally, the article provides some tips for managing a backlog of analytical work and for tracking progress and results. Overall, the article offers practical advice for data analysts and other professionals responsible for managing analytical workloads. Read more here

Thanks for reading! Now it’s your turn: Which article did you love the most and why?