Best of Data Warehouse — 2024
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Community Picks·2y
Simplifying Your Tech Stack with PostgreSQL
Simplifying your tech stack with PostgreSQL can streamline development, reduce operational complexity, and minimize moving parts. PostgreSQL can efficiently replace multiple technologies such as Kafka, RabbitMQ, MongoDB, and Redis, supporting functionalities like caching, message queuing, data warehousing, and full-text search. This approach enhances developer productivity, reduces cognitive load, and ensures robust performance and flexibility. Developers benefit from PostgreSQL's comprehensive support for JSON, geospatial queries, auditing, and more, providing a powerful and scalable backend solution.
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Data Engineer Things·2y
I spent 5 hours learning how Google manages terabytes of metadata for BigQuery.
Google BigQuery uses innovative techniques to manage massive amounts of metadata efficiently, treating it as crucial as the data itself. BigQuery's architecture includes Colossus for storage, Dremel for querying, and a dedicated shuffle service, all coordinated by Borg. Metadata is handled in a distributed manner using a unique columnar storage format called CMETA, improving efficiency and performance. Real-time data ensures physical query plans adapt dynamically for optimized results, while integrated metadata scans enhance query processing.
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I spent 5 hours learning how ClickHouse built their internal data warehouse.
ClickHouse has built an internal data warehouse to handle 50 TB of data daily, incorporating multiple internal sources like AWS, GCP, and Salesforce. They use Airflow for scheduling, AWS S3 as the intermediate data layer, and Superset for BI tools. Key features include data consistency, idempotency, and real-time analytics. They have also adopted dbt to streamline data transformations and introduced new tools for improved user access to data.
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Hacker News·2y
Stripe Data vs Open‐Source Alternatives: a MRR example
Stripe's API lacks straightforward methods for calculating MRR and necessitates the use of additional costly tools like Stripe Sigma and Stripe Data Pipeline. These tools are ideal for large companies with substantial transactions but it's impractical for smaller transactions due to high costs. Open-source alternatives, such as Lago, provide more flexibility and control over financial data, avoiding dependence on expensive third-party solutions.
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The Data Lake, Warehouse and Lakehouse
The post explores the evolution of data architecture, beginning with traditional data warehouses, followed by the introduction of data lakes, and culminating in the emergence of the Lakehouse paradigm. It highlights the limitations of data warehouses and data lakes, such as challenges with unstructured data and data staleness. The Lakehouse architecture aims to combine the best features of both by utilizing low-cost storage and enhancing management features such as ACID transactions and query optimization. The post also mentions various technologies like Delta Lake, Apache Hudi, and Apache Iceberg that facilitate efficient data management in Lakehouse architectures.
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Substack·2yData pipelines and SCDs
Designing backfillable data pipelines using idempotent transformation code avoids the complications of ad-hoc SQL. When handling Slowly Changing Dimensions (SCDs), SCD Type 2 is preferred for its immutability and compressive qualities, though it involves complex surrogate key lookups. Alternatively, snapshot tables offer a simpler, reproducible model at the cost of higher data replication, making them ideal in cloud environments where storage is cheaper than engineering time.
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SQL Shack·2y
Finding Duplicates in SQL
This post explains the different ways to find duplicate values in SQL using DISTINCT and COUNT, GROUP BY and COUNT, and ROW_NUMBER functions. It provides examples and guidance on how to use these functions to identify duplicates in single columns or across multiple columns. The post also highlights the importance of managing duplicates in data storage and processing.
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MotherDuck·2y
The Data Warehouse powered by DuckDB SQL
MotherDuck combines the power of DuckDB SQL with cloud services to offer a flexible and powerful data warehousing solution. It includes robust capabilities for data ingestion, transformation, and analysis, leveraging SQL and additional native Python APIs for complex tasks. Its built-in AI features enhance usability for business users, data scientists, and developers. MotherDuck supports a wide range of file formats and storage solutions, and offers advanced analytical functions, including Machine Learning algorithms, to solve complex business problems efficiently.
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Towards Data Science·2y
Data Modeling Techniques For Data Warehouse
Data modeling is a key process in creating conceptual representations of organizational data and its relationships. Focusing on various methodologies like Kimball's, Inmon's, and Data Vault, this guide provides insights into dimensional modeling, including benefits like simplicity, improved query performance, and scalability. It also covers different schema types (star and snowflake), and strategies for data loading. Special attention is given to innovative approaches like using one big table (OBT) for modern data warehouses.
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databricks·2y
Introducing the New SQL Editor
Databricks announces the public preview of its new SQL editor, designed to enhance productivity with features like multiple statement results, real-time collaboration, improved assistant integrations, and enhanced editor ergonomics. New functionalities such as Quick Fix, AI-generated filters, and Git support for queries are also introduced, aiming to streamline SQL development and collaboration.
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SingleStore·2y
Designing a Real-Time Data Warehouse
In the era of data-driven applications, real-time data warehouses (RTDW) are crucial for enabling low-latency analytical queries on fresh data. Unlike traditional data warehouses, RTDWs support continuous data ingestion and high concurrency, making them essential for applications like fraud detection and market analysis that require immediate insights. SingleStore offers a robust RTDW solution with real-time data ingestion, low-latency processing, high-concurrency support, scalability, and seamless integration, delivering real-time analytics at scale.
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Materialized View·2y
DuckDB Is Not a Data Warehouse
DuckDB is a highly portable and fast tool for handling columnar data, often used by analytics and data engineers for various creative purposes. However, it is not considered a viable solution for large enterprise data warehousing due to its deployment model and limited scalability. MotherDuck aims to address these issues by building a centralized deployment model but faces tough competition from established cloud data warehouses like Snowflake and BigQuery, as well as PostgreSQL extensions.
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Building an open data pipeline in 2024
Building an open data pipeline in 2024 involves understanding requirements for data scale, latency, governance and access controls, and cost. By using Iceberg as the core data storage layer, you can leverage different compute environments and achieve flexibility and cost-effectiveness.
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The Many Data Problem: Is Your Company Struggling with too much Data?
Companies are now facing a 'Many Data problem' due to the ease of data creation and increasing reliance on data for business decisions. Challenges include lack of data interoperability, excessive and unvaluable dashboards, a need for data governance, rising cloud data warehouse costs, and poor data quality. Focusing on improving interoperability, reducing unnecessary dashboards, implementing governance, optimizing costs, and enhancing data quality can help manage this problem effectively.
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Towards AI·2y
Journey From Data Warehouse To Lake To Lakehouse
The post provides a fictional story to simplify the understanding of data storage concepts such as Data Warehouse, Data Lake, and Data Lakehouse. It highlights the evolution from the structured data storage of Data Warehouses, to the flexible, low-cost storage of Data Lakes, and finally to the comprehensive and efficient storage solutions of Data Lakehouses, which combine the benefits of both previous systems. Key concepts like schema-on-read and schema-on-write are explained, and top providers for each storage solution are recommended.
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Data Science Central·2y
Role of AI in Building Data Warehouses
Leveraging AI in data warehousing offers multiple benefits including automation, enhanced efficiency, improved data quality, and optimization of the querying process. It aids in data integration, modification, and ETL processes while ensuring consistent and reliable data. AI enhances security by detecting unusual behaviors and helps in scaling the data warehouse seamlessly with cloud integration.
