The Orchestra Data Leadership Newsletter

The Orchestra Data Leadership Newsletter focuses on insights and methodologies in data management and leadership, emphasizing data orchestration, governance, and the integration of emerging technologies like AI and machine learning. It covers practical guides, industry trends, and tool evaluations aimed at enhancing the effectiveness of data teams.

Data Governance Data Orchestration Machine Learning and AI Data Engineering Practices Data Product Management Data Quality Web Scraping Data Architecture Cloud Infrastructure Data Tools Evaluation Data Pipelines Data Strategy Generative AI in Data Engineering

The hottest Substack posts of The Orchestra Data Leadership Newsletter

And their main takeaways

Understanding your Big Data problem: Transforming Big Data with Incremental Models

0 implied HN points • 31 Oct 23
🕹 Technology Data Engineering Data Modeling SQL Testing
  1. Understanding the importance of incremental models for managing big data is crucial to efficiently running complex queries and maintaining data quality.
  2. Design patterns in data modeling, such as Star Schema and Data Vault, play a significant role in how dbt models are structured and managed.
  3. Using Jinja templating and implementing continuous data integration processes are key elements in handling big models effectively and ensuring data reliability.

Are Lakehouses a joke or is Databricks the endgame??

0 implied HN points • 19 Oct 23
🕹 Technology Data Engineering Data Warehousing Machine Learning Real-Time Processing
  1. Considering the evolution of data engineering tools and software can be likened to the concept of limits in mathematics, where processes tend to 'streaming' use cases and Lakehouses play a role in this transition.
  2. Databricks, developed by the creators of Apache Spark, excels in loading data from Data Lakes, handling schemas, and treating data sources as streams, making it a valuable tool for data processing.
  3. While Databricks offers advanced capabilities in data ingestion, transformation, and machine learning operations, there may still be a need for custom infrastructure for specific real-time use cases, leading to a nuanced evaluation of tools like Databricks in the data engineering landscape.

Data Leadership #5 How to lead your data team like a boss

0 implied HN points • 17 Oct 23
💼 Business Leadership Team Management Pressure
  1. Managing a data team can be challenging due to the breadth of responsibilities, team size, and tasks' nature, which require a project-focused approach to work.
  2. Aligning expectations with stakeholders, particularly those with low data literacy, is crucial for effective leadership in a data team.
  3. Investing in individual development, avoiding tribal knowledge, and focusing on project management can help mitigate challenges related to team size, pressure, and upskilling.

Data Leadership #4 knowing when to rip out your infrastructure

0 implied HN points • 15 Oct 23
🕹 Technology Data Operations Cloud Computing Business Intelligence Data architecture
  1. Knowing when to shift left on security is crucial to preventing data breaches and maintaining a secure network infrastructure.
  2. Re-evaluating the usefulness and uptake of self-service analytics tools can help in optimizing resources and avoiding unnecessary costs.
  3. Carefully analyzing cloud warehouse costs and data movement can lead to cost savings and efficient data management.

Data Leadership #1 Why being a Head of Data isn't what you think it is

0 implied HN points • 04 Oct 23
🕹 Technology Data Management Data Strategy Data Literacy
  1. Being a Head of Data involves more than just solving problems, it requires aligning stakeholders, data cleanliness, and resources.
  2. Responsibilities as a Head of Data may shift towards evangelizing data tools, advocating for data strategy, and applying domain knowledge to solve business problems.
  3. Data leadership in a less mature data environment should focus on hitting crucial data use cases, getting leadership buy-in, and marketing the value of data within the organization.
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How we think about Data Pipelines is changing

0 implied HN points • 08 Nov 23
🕹 Technology Data Pipelines Continuous Integration Infrastructure Observability
  1. Data pipelines are transitioning towards a focus on reliability and efficiency, similar to software engineering practices.
  2. Continuous Data Integration and Delivery in data engineering involves releasing data into production in response to code changes in a simple manner.
  3. Observability and metadata gathering play a crucial role in ensuring data quality and preventing issues before they occur in data pipelines.

Coming soon

0 implied HN points • 04 Oct 23
💼 Business Leadership
  1. The post is promoting a newsletter on data leadership by Hugo Lu.
  2. The newsletter is called The Orchestra Data Leadership Newsletter.
  3. Readers are encouraged to subscribe for updates on data ops leadership.

What is open-source workflow orchestration and when should you move on?

0 implied HN points • 23 Oct 23
🕹 Technology Data Engineering Workflow Orchestration Open Source Data Operations Data science
  1. Open-source workflow orchestration tools like Apache Airflow have been around for a long time and offer flexibility in developing, scheduling, and monitoring batch-oriented workflows.
  2. Specialized tools are emerging for data operations to improve quality, moving away from the Swiss Army Knife approach of general-purpose orchestration tools.
  3. When considering upgrading from open-source orchestration tools, evaluate if the tool effectively handles monitoring, metadata gathering, and other complex data operation needs; specialized tools may be more suitable in such cases.