Data 101 Building an Insights page for your SaaS: from idea to production I've helped so many people add an Insights page or dashboard to their app. Here are the steps to take your user-facing analytics from idea to prod.
Tech Top Use Cases for DynamoDB in 2024 DynamoDB… it's fast, scalable, and flexible. What's not to love? Here are the top use cases for DynamoDB in 2024 (and a few areas where it won't work).
Data 101 Best practices for timestamps and time zones in databases Confused about how to handle dates, times, and DateTimes in your database? We’ve got you covered, now() and always.
Real-time Data Featured User-Facing Analytics: Examples, Use Cases, and Resources User-facing analytics is the practice of embedding real-time data visualizations into user-facing applications. Learn more about user-facing analytics and how it's built in this definitive guide.
Product Run analytics on files in Amazon S3 the easy way So, you want to run analytics on data stored in S3 files? Here’s the easy way to do it, using the Tinybird S3 Connector.
Data 101 Event-driven architecture best practices for databases and files Event-driven architectures should have largely replaced poll-based workflows and batch ETLs. Here are the common patterns (and anti-patterns) I have observed for event-driven ingestion of data from both application databases and file systems.
Data 101 Using Tinybird as a serverless online feature store Machine learning can feel like a lot of hype, but feature stores are extraordinarily practical. In this post, I'll show you why Tinybird makes for an excellent online feature store for real-time inference and game-changing user experiences.
Data 101 5 criteria of data quality and how to test for them Data Quality Assurance is an important focus for companies seeking to advance the trustworthiness of their data pipelines. Here are 5 criteria for measurement data quality, and some sample SQL you can use to test for them.
Data 101 The 8 considerations for designing public data APIs For years I developed flood warning systems built on public data APIs. From my experience, I learned that there are 8 things you must consider to build a resilient public data API. These are those 8 things, and how Tinybird can take some of them off your plate.
Data 101 The 5 rules for writing faster SQL queries If you're building real-time analytics, you need your SQL to be fast. Fast SQL queries improve performance and reduce cost. Here are the 5 rules to follow to write the fastest SQL queries of your life.
Data 101 Understanding the Data Warehouse What gave rise to the Data Warehouse? What do they enable? I answer these questions in this first of a two-blog series.
Product Comparing Tinybird to AWS Kinesis, S3, Glue, and Athena Amazon S3, Kinesis, Glue, and Athena are often used for strategic data analysis. Learn how Tinybird fits in the AWS stack for real-time, operational analytics.
Data 101 What is a data product? Like a song recorded in a studio, a data product is more than the sum of its parts.
Data 101 The data rules worth $40,000 a day A "real-world" example of how data best practices can seriously shrink your usage bill.
Data 101 Roll up data with Materialized Views How I used rollups and Materialized Views in Tinybird to dynamically track transactions made on the Ethereum blockchain.
Data 101 SQL and Python: alerts from predictions Combine Tinybird with pre-coded models to make predictions, compare data in real time to the predictions and alert.
Data 101 Starting with Kafka I just want to share my thoughts on Kafka after using it for a few months, always from a practical point of view. I don’t know anything more than the basics ...
Data 101 DataOps: How to Develop and Scale Data Intensive Projects As we build Tinybird, we work hand in hand with many data and engineering teams. In the process we are discovering new ways to develop, maintain and scale da...
Data 101 DataOps: 10 principles to develop data intensive projects 10 of the principles of DataOps that we make available to data teams.
Data 101 Typical Challenges of Building Your Data Layer When you start a digital product you usually put your data in a database. It does not matter if it is a simple text file, an excel spreadsheet or a managed Postgres instance on the cloud, your data always lives somewhere.
