The hottest Data Warehousing Substack posts right now

And their main takeaways
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Common Data Pipeline Patterns You’ll See in the Real World

SeattleDataGuy’s Newsletter • 859 implied HN points • 05 Jan 26
🕹 Technology Data Warehousing
  1. Data pipelines come in many shapes — from source standardization and amalgamation to enrichment, operational syncs, and even manual Excel-based processes — each built for different business needs.
  2. Common challenges are mapping and standardizing varied formats, keeping reliable IDs and timing for joins, and handling data quality and system-specific ingestion limits.
  3. Despite the variety, pipelines all aim to move and transform source data into usable outputs for analytics, operations, or ML, and they often follow the same extract-transform-load steps that can be automated and productionized.

Leaving product analytics

timo's substack • 314 implied HN points • 05 Jun 23
🕹 Technology Data Warehousing
  1. Product analytics tools like Amplitude, Mixpanel, and Heap are evolving to offer new features like marketing attribution and user experience analytics.
  2. New players in the market like Kubit are focusing on providing product analytics directly on cloud data warehouses.
  3. The future of analytics is moving towards event analytics, opening up new possibilities and challenges for businesses.

I spent 5 hours learning how ClickHouse built their internal data warehouse.

VuTrinh. • 1 HN point • 21 Sep 24
🕹 Technology Data Warehousing
  1. ClickHouse built its internal data warehouse to better understand customer usage and improve its services. They collected data from multiple sources to gain valuable insights.
  2. They use tools like Airflow for scheduling and Superset for data visualization, making their data processing efficient. This setup allows them to handle large volumes of data daily.
  3. Over time, ClickHouse evolved its system by adding dbt for data transformation and improving user experiences with better SQL query tools. They also incorporated real-time data to enhance their reporting.
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Why Apache Iceberg is heralding a new era of change in Data Engineering

The Orchestra Data Leadership Newsletter • 59 implied HN points • 20 Mar 24
🕹 Technology Data Warehousing
  1. Apache Iceberg introduces Bring Your Own Storage (BYOS) concept, which is gaining popularity for efficient and reliable data management in distributed environments.
  2. Key features of Apache Iceberg include Atomic Transactions, Schema Evolution, Partitioning and Sorting, Time Travel, Incremental Data Updates, Metadata Management, and Compatibility with various data processing frameworks.
  3. Platforms like Snowflake are shifting towards supporting Iceberg due to its benefits in handling data efficiently and enabling a Bring Your Own Storage pattern.

Simple, Modern, and Modular: Data Stacks for scrappy Businesses

The Orchestra Data Leadership Newsletter • 39 implied HN points • 12 Jan 24
🕹 Technology Data Warehousing
  1. Building data stacks for businesses involves using core software like Snowflake and Databricks, focusing on delivering business value efficiently.
  2. The recommended tools include DIY cloud solutions for streaming, Snowflake for transformations, and BigQuery or Snowflake for storage/warehouse needs.
  3. Using a comprehensive tool like Orchestra can facilitate end-to-end data pipeline management, without requiring a large data team and providing cost-effective solutions.

Common Techniques For Periodically Extracting Data From Relational Databases

Practical Data Engineering Substack • 59 implied HN points • 18 Sep 23
🕹 Technology Data Warehousing
  1. Data extraction from relational databases is important for building data pipelines. It's key to choose the right method based on factors like the type and size of the data.
  2. There are several techniques to detect changes in the data, including using timestamps and database triggers. These help identify what new or changed records need to be extracted.
  3. Different data extraction patterns exist, like periodic full reloads or incremental updates. Each method has its pros and cons, and the choice depends on the specific data needs and conditions.

sqlmesh model kinds - 2

davidj.substack • 47 implied HN points • 09 Dec 24
🕹 Technology Data Warehousing
  1. There are three types of incremental models in sqlmesh: Incremental by Partition, Unique Key, and Time Range. Each type has its own unique method for handling how data updates are processed.
  2. Incremental models can efficiently replace old data with new data, and sqlmesh offers better state management compared to other tools like dbt. This allows for smoother updates without the need for full-refresh.
  3. Understanding how to set up these models can save time and resources. Properly configuring them allows for collaboration and clarity in data management, which is especially useful in larger teams.

The Modern Data Stack is Dead… Long Live the Modern Data Stack - Part 1

davidj.substack • 167 implied HN points • 19 Jul 23
🕹 Technology Data Warehousing
  1. The Modern Data Stack (MDS) community has grown significantly over the years with various meetups and events.
  2. Using tools like Snowflake, dbt, and Looker in the Modern Data Stack improves data capabilities and productivity.
  3. Although some criticize the Modern Data Stack and its imperfections, it has greatly enhanced data handling and analytics for many organizations.

Streaming in real life

davidj.substack • 47 implied HN points • 23 Feb 24
🕹 Technology Data Warehousing
  1. Real-time data streaming from databases like MySQL to data warehouses such as Snowflake can significantly reduce analytics latency, making data processing faster and more efficient.
  2. Streamkap offers a cost-effective solution for streaming ETL, promising to be both faster and more affordable than traditional methods like Fivetran, providing a valuable option for data professionals.
  3. Implementing Streamkap in data architectures can lead to substantial improvements, such as reducing data update lag to under 5 minutes and delivering real-time analytics value for customers, showcasing the impact of cutting-edge data technology.

Microsoft Fabric Mirroring: Revolutionising Data Access and Real-Time Insights

Data Plumbers • 2 HN points • 01 Apr 24
🕹 Technology Data Warehousing
  1. Microsoft Fabric Mirroring is a transformative technology that revolutionizes data access and real-time insights in organizations.
  2. Mirroring enables universal access to various databases, real-time data replication, and granular control over data ingestion into Microsoft Fabric's Data Warehousing experience.
  3. With Mirroring, organizations can achieve zero-ETL insights, leverage the innovative capabilities of Fabric's OneLake repository, and bridge the gap between data and action for swift adaptation and success.

The Iceberg that sank the Snowflake?

Brick by Brick • 9 implied HN points • 01 Mar 24
🕹 Technology Data Warehousing
  1. Snowflake's stock dropped significantly after the announcement of CEO Frank Slootman's retirement, with a key concern being the impact of Apache Iceberg on moving data out of Snowflake.
  2. Apache Iceberg is a powerful technology that allows for the efficient migration of data out of Snowflake to other systems for processing, causing revenue loss in both storage and compute for Snowflake.
  3. The paradigm shift towards technologies like Iceberg takes time in enterprise settings but can have a significant impact, highlighting the importance of capturing the compute dollars in data processing.

Khải Trần: tư duy ngắn hạn - tư duy dài hạn qua câu chuyện xây kho dữ liệu trên mây

Thái | Hacker | Kỹ sư tin tặc • 39 implied HN points • 21 Oct 18
🕹 Technology Data Warehousing
  1. Different business strategies: Amazon focused on being the first with Redshift, while Snowflake prioritized a new architecture separating computation and storage for a more scalable system.
  2. Cloud computing advantages: Cloud services like Redshift and Snowflake offer flexibility, cost-effectiveness, and scalability compared to traditional on-premise data warehouses.
  3. Market competition: Amazon leads the cloud market with Microsoft following closely, while Google is catching up with strong computing infrastructure despite starting later.

Mastering the B2B Analytics Stack from Founding to Growth stage: A Comprehensive Guide

nonamevc • 6 HN points • 22 Mar 23
🕹 Technology Data Warehousing
  1. Consider the timing and readiness of your organization before implementing new tools in the B2B analytics stack.
  2. In the founding stage, focus on qualitative data, understanding customer needs, and building a customer profile.
  3. During the growth stage, invest in sophisticated analytics tools, like data warehouses and experimentation platforms, to effectively manage growing data.

Why has managing email in PLG B2B SaaS become such a tedious job?

nonamevc • 3 HN points • 05 Feb 24
🕹 Technology Data Warehousing
  1. There are multiple types of emails involved in PLG B2B SaaS, including transactional, product-related, and marketing campaigns.
  2. Challenges in managing email in PLG B2B SaaS include the need for orchestration, tedious content management, and high data volume.
  3. Companies like Inflection and Humanic offer new perspectives and solutions to address the complexities of managing email in PLG B2B SaaS.

Migrating a data warehouse

Expand Mapping with Mike Morrow • 0 implied HN points • 27 Feb 26
🕹 Technology Data Warehousing
  1. A warehouse migration is a multi-step project where tasks range from easy to very hard. Some small changes like updating BI connections are quick, but others need significant effort.
  2. Medium-effort work like schema mapping, one-time backfills, and reconfiguring pipelines is necessary and requires careful data validation. These steps are manageable but time-consuming.
  3. The hardest parts are deciding what data to keep, rewriting transformations, running both warehouses in parallel, and recreating access controls. Those areas carry the most risk and will dominate the timeline.

Are Lakehouses a joke or is Databricks the endgame??

The Orchestra Data Leadership Newsletter • 0 implied HN points • 19 Oct 23
🕹 Technology Data Warehousing
  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 #2 Understanding Data Lake Architecture

The Orchestra Data Leadership Newsletter • 0 implied HN points • 08 Oct 23
🕹 Technology Data Warehousing
  1. Understanding the architectural structure of data lakes is crucial for data leaders to make informed decisions on data storage.
  2. File formats play a significant role in data storage efficiency, querying capabilities, and overall costs in a data lake architecture.
  3. Choosing between data lake providers or data warehouses can be complex due to the influence of underlying technologies, like object stores and file formats.

Implementing ETL on GCP

realkinetic • 0 implied HN points • 15 Jul 20
🕹 Technology Data Warehousing
  1. ETL processes are vital for data analytics, involving extracting, transforming, and loading data for storage in a warehouse.
  2. GCP offers options like Data Fusion and Cloud Dataprep for implementing ETL pipelines, catering to varying technical skill levels and preferences.
  3. Alternative approaches on GCP for ETL include using services like Cloud Dataflow for more code-heavy processes or leveraging BigQuery for ELT if your team is SQL-focused.