SwirlAI Newsletter

SwirlAI Newsletter focuses on end-to-end Data Systems, covering topics from Data Engineering fundamentals to advanced MLOps deployment processes. It includes guides on optimizing Spark application performance, understanding vector databases, managing data freshness, as well as organizational structures for effective MLOps and strategies for efficient machine learning experimentation environments.

Data Engineering MLOps Machine Learning Spark Optimization Vector Databases Data System Scalability Data Freshness in ML Systems Organizational Structure for ML Projects Data System Decomposition Data Value Chain Stream Processing

The hottest Substack posts of SwirlAI Newsletter

And their main takeaways

A Guide to Optimising your Spark Application Performance (Part 1).

432 implied HN points • 02 Jul 23

Understanding Spark architecture is crucial for optimizing performance and identifying bottlenecks.
Differentiate between narrow and wide transformations in Spark, and be cautious of expensive shuffle operations.
Utilize strategies like partitioning, bucketing, and caching to maximize parallelism and performance in Spark applications.

SwirlAI Table of Contents

432 implied HN points • 28 Jun 23

🕹 Technology Data Engineering MLOps System Design Systems Thinking

The newsletter provides a Table of Contents with more than 90 topics, making it easier to find the content of interest.
Topics covered include Data Engineering fundamentals, Spark architecture, Kafka use cases, MLOps deployment processes, System Design examples, and more.
If interested, it's recommended to support the author's work by subscribing and sharing the content.

SAI Notes #04: CI/CD for Machine Learning.

511 implied HN points • 28 May 23

🕹 Technology Machine Learning DevOps Organizational Structure

In Machine Learning projects, CI/CD processes need to treat the ML training pipeline separately from regular software pipelines.
Efficient MLOps implementation requires an organizational structure where ML product development flows within a single end-to-end ML team.
ML systems in mature MLOps setups involve ML teams building and delivering pipelines that expose predictions to end users through backend and frontend services.

A Guide to Optimising your Spark Application Performance (Part 2)

314 implied HN points • 06 Aug 23

🕹 Technology Programming Big Data Optimization Data Storage

Choose the right file format for your data storage in Spark like Parquet or ORC for OLAP use cases.
Understand and utilize encoding techniques like Run Length Encoding and Dictionary Encoding in Parquet for efficient data storage.
Optimize Spark Executor Memory allocation and maximize the number of executors for improved application performance.

Levels of Data Freshness in Machine Learning Systems

373 implied HN points • 09 Jul 23

🕹 Technology Data Engineering Machine Learning

Data freshness is crucial in machine learning systems to provide accurate and valuable insights.
Different levels of feature freshness exist in ML systems, each with its own investments and complexities.
Starting with simpler models and gradually moving to more real-time systems can be more cost-effective and efficient.

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SAI Notes #07: What is a Vector Database?

412 implied HN points • 18 Jun 23

🕹 Technology Data Engineering Machine Learning Big Data Database Management Data Storage

Vector Databases are essential for working with Vector Embeddings in Machine Learning applications.
Partitioning and Bucketing are important concepts in Spark for efficient data storage and processing.
Vector Databases have various real-life applications, from natural language processing to recommendation systems.

SAI #26: Partitioning and Bucketing in Spark (Part 1)

373 implied HN points • 15 Apr 23

🕹 Technology Data Engineering Big Data Performance optimization Data Storage Data processing

Partitioning and bucketing are two key data distribution techniques in Spark.
Partitioning helps improve performance by allowing skipping reading the entire dataset when only a part is needed.
Bucketing is beneficial for collocating data and avoiding shuffling in operations like joins and groupBys.

SAI #28: Organisational structure for effective MLOps.

353 implied HN points • 29 Apr 23

🕹 Technology Organizational Structure

The structure of ML project involves ideation, experimentation, deployment, and monitoring stages.
End-to-end machine learning teams are essential for efficient ML product delivery.
Cognitive load within ML teams evolves as projects progress, leading to the introduction of ML platform teams.

SAI Notes #05: Building efficient Experimentation Environments for ML Projects.

275 implied HN points • 04 Jun 23

🕹 Technology Data Infrastructure

Experimentation Environments in MLOps are crucial for improving ML model development velocity.
Efficient Experimentation Environments should provide access to raw and curated data for Data Scientists.
MLOps tooling has matured, with a focus on point solutions rather than end-to-end platforms.

SAI #22: Decomposing the Data System.

294 implied HN points • 18 Mar 23

🕹 Technology Data science Data Engineering MLOps Machine Learning Data Systems

Learning to decompose a data system is crucial for better reasoning and understanding of large infrastructure
Decomposing a data system allows for scalability, identification of bottlenecks, and total event processing latency optimization
The different layers in a data system include data ingestion, transformation, and serving layers, each with specific functions and technologies

SAI Notes #01: Watermarks in Stream Processing, SQL Query order of Execution.

255 implied HN points • 07 May 23

🕹 Technology Data processing Stream Processing Data Engineering Data Systems

Watermarks in Stream Processing help handle event lateness and decide when to treat data as 'late data'.
In SQL Query execution, the order is FROM and JOIN, WHERE, GROUP BY, HAVING, SELECT, ORDER BY, LIMIT.
To optimize SQL Queries, reduce dataset sizes for joins and use subqueries for pre-filtering.

SAI #19: The Data Value Chain.

255 implied HN points • 25 Feb 23

🕹 Technology Data Engineering MLOps Machine Learning Data Contracts

Understanding the Data Value Chain is essential for building successful Data Products.
Implementing Data Contracts in the Data Pipeline ensures data quality and prevents unexpected outages.
Knowing the 4 types of ML Model Deployment helps in deploying machine learning models effectively.

SAI #27: Event Latency in Data Systems

255 implied HN points • 22 Apr 23

🕹 Technology Data Systems Data Collection

Data latency can unexpectedly increase at any stage of the Data Pipeline.
It is essential to decompose the Data System into smaller elements to identify and fix bottlenecks efficiently.
Effective monitoring of each component is necessary to avoid breaches in SLAs.