Materialized view is the cache of views. It pre-computes, stores, and optimizes data access when created, and automatically refreshes to ensure real-time data availability.

Materialized view pattern

Learn how to transition from application development to data engineering by building a modern project reporting system. Discover essential mindset shifts, best practices, and hands-on look using tools like DuckDB, and Modal. Master system-level architecture and data pipeline design for scalable enterprise solutions.

A Lens to Modern Data Engineering

Building a data-driven project reporting system: A lens into modern data engineering

Learn how the ACID model ensures database transaction reliability with atomicity, consistency, isolation, and durability for accurate, secure, and concurrent data management in ACID-compliant systems.

Acid model

Learn what Big Data is by exploring the 3 V's—Volume, Velocity, and Variety—and discover how Hadoop provides scalable, cost-effective solutions for storing and processing massive data sets.

Apache Hadoop and big data

Learn how to create and understand Hive tables, including table structure, storage formats, SerDe functions, HDFS data location, and metadata management in the Hive metastore.

Behind a hive table

A Bloom filter is a probabilistic data structure used for testing whether an element is a member of a set or not. It's space-efficient compared to other data structures like hash tables, but it may give false positives (indicating that an element is in the set when it's not) and never gives false negatives (indicating that an element is not in the set when it actually is not)...

Bloom filter

Learn how Apache Hive buckets solve issues with dynamic partitions by reducing tiny files, enabling efficient sampling, and improving query performance through bucketing and partitioning techniques.

Buckets on Apache Hive

Learn about the CAP theorem, which explains how distributed databases balance consistency, availability, and partition tolerance, and how different database types prioritize these guarantees.

Cap theorem

Explore how we developed a fully local search engine for our memo website using DuckDB-wasm, Transformers.js, and Alpine.js. Learn about hybrid search techniques, real-time embeddings, and performance optimizations that deliver fast and accurate results without compromising on privacy or requiring server-side processing.

Building a local search engine for our Memo website

Discover how data analysts use tools like Power BI and SQL to transform retail trading data into actionable business insights on sales, finance, and marketing performance.

Data analyst in retail trading

To improve and strongly go-live the data pipeline, besides apply best practices and pillar for Data Pipeline Native Solution, a design framework and pattern are robustly help us in...

Data pipeline design framework

Learn how Data Vault architecture builds flexible, scalable data lakes using hubs, links, and satellites to manage business keys and historical data with pros and cons explained.

Data vault modelling

A comprehensive guide to understanding circular references in database design, including types, challenges, real-world applications, and solutions for managing them effectively. Covers self-references, circular dependencies, and strategies for maintaining data integrity while working with circular relationships.

Database design circular

Learn how explicit locking techniques in PostgreSQL help manage concurrency control and prevent data conflicts in high workload databases by using table-level, row-level, and advisory locks effectively.

Database locking

Discover how DBT (data build tool) simplifies data transformation in warehouses with modular SQL, automation, and software engineering practices for faster, reliable analytics and trusted data delivery.

Dbt the good solution to accelerate data transformation

In this part, I will discuss how I handle the logic and design the data model for the following features: setting permissions, sharing files for public access, and setting a password for a file.

Design file-sharing system - part 2: permission & password

When we started working on this project aimed at the creative community, we faced an interesting challenge. We needed to build a model that was similar to a task but with a key difference: it had to support custom, dynamic, and extensible properties. If you’ve ever used Notion and appreciated how flexible its objects are, you’ll know exactly what we were trying to achieve.

Designing a model with dynamic properties

As systems scale to meet growing demands, databases must evolve alongside them to maintain performance and integrity. This document explores best practices for managing database changes, maintaining knowledge, and ensuring smooth integration. Topics covered include knowledge sharing, repository structuring, continuous integration, and database refactoring, with real-world examples illustrating their application.

Evolutionary database design: managing change and scaling with the system

Learn how to boost PostgreSQL search performance using full-text search with GIN indexes, stop words, and custom configurations for faster and flexible text queries on large datasets.

Full text search with postgresql

Learn why Hadoop Distributed File system HDFS is essential for big data, enabling block-based storage, fault tolerance through replication, and scalable distributed file management across clusters.

Hadoop distributed file system hdfs

Learn how to use Hive window and analytical functions to calculate complex metrics like a 10-day moving average on stock data with easy-to-understand SQL examples and syntax.

Hive window and analytic functions

Learn how Discord migrated from MongoDB to Cassandra for scalable, low-maintenance data storage, handling billions of messages with predictable performance and eventual consistency.

How discord stores messages part 1 from mongodb to cassandra

Learn how Apache Hive simplifies big data analysis by enabling SQL queries on Hadoop datasets using tables, making MapReduce easier for developers and data analysts.

Introduction to Apache Hive

Apache Pig simplifies Hadoop data processing by converting easy-to-write Pig Latin scripts into MapReduce jobs, enabling non-programmers to handle big data without coding in Java or Python.

Introduction to Apache Pig

Conflict-free Replicated Data Types (CRDTs) (aka convergent replicated data type or commutative replicated data type) are data structures that can be replicated across multiple computers in a network, where the replicas can be updated independently and concurrently without coordination between them, and enable operations to always converge to a final state consistent among all replicas.

Introduction to CRDT

Local-first software is an approach to software development that emphasizes data ownership, offline functionality, and real-time collaboration. This model ensures data is primarily stored on the user's device, enhancing privacy and control while enabling seamless synchronization and collaboration without relying on continuous internet connectivity

¶ Local-first software

Learn the key differences between Hive managed tables and external tables, including when to use each type and how dropping tables affects their underlying data in HDFS.

Managed table vs external table

Learn how MapReduce processes large stock data to find maximum closing prices using map, shuffle, reduce phases, input splits, and optional combiners for efficient distributed computing and performance.

Mapreduce components

We are the head of census bureau for the state of California and tasked with finding the finding the population of all cities in California. All the resources we want are ready but we have only four months to finish the task. Calculating the population of all cities for a big state like california is not an easy task. The sensible thing to do is to divide the state by city and make individuals in charge of each city to calculate the population of each city where he is in charge of...

¶ MapReduce

Learn how to optimize Hive queries by using ORDER BY, SORT BY, DISTRIBUTE BY, and CLUSTER BY to efficiently order large datasets with multiple reducers and improve performance.

Order by vs sort by vs distribute by vs cluster by

Learn how Hive partitions and dynamic partitions optimize query performance by targeting specific data subsets, reducing scan times, and efficiently managing large datasets for faster Hive queries.

Partitions on Apache Hive

Learn how to build a fast user leaderboard system using Redis sorted sets and hashes to track scores and store user profiles like usernames and avatars.

Redis leaderboard

Learn how trees, especially binary search trees (BST) and AVL trees, organize hierarchical data for fast search, insertion, and deletion with guaranteed balanced height and efficient operations.

Self balanced bsts avl trees

Sequential read/write operations involve accessing data in a continuous, linear manner. This typically occurs when transferring large files or accessing a large file on the drive. In sequential read/write operations, the drive can read or write data from a series of blocks, allowing for faster performance...

Sql and how it relates to disk reads and writes

Structured Query Language (SQL) is widely used for managing data in relational database management systems (RDBMS). In the context of SQL, the optimization of database queries forms the foundation for efficient data retrieval, providing quicker response times and increasing overall database performance. One key strategy to optimize SQL queries involves the concept of "Sargable" queries...

SQL saragable queries and their impact on database performance

Learn how to speed up Binance Kline API data retrieval by using a cached table to efficiently get highest and lowest cryptocurrency prices and timestamps within large date ranges.

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