Discover what streaming analytics is and how you can use Azure, AWS, and Kafka to analyze and visualize data for better insights & decisions.

Is connecting SQL Server to Snowflake essential for the success of your data stack? Choose your favorite from these 4 step-by-step methods.

Learn how to enable Change Data Capture in SQL Server with our detailed guide. We’ll show you how to set up CDC and use it to track changes.

Learn two no-fuss methods to move your MongoDB data into BigQuery — step-by-step instructions included.

Choosing the right database can have a HUGE effect on your application. Let’s break down the pros and cons of PostgreSQL and MongoDB.

Everything you need to know about data streaming technology, from best practices to real-life examples of how it’s used.

Streaming data infrastructure is notoriously hard to manage. DataOps gives you the framework to do so successfully.

PostgreSQL has all the standard RBDMS data types, and then some. Use this handy reference for Postgres data type syntax, examples, and more.

Microsoft SQL Server provides a native CDC capability, but that’s far from the whole story. Learn what’s possible and how to take advantage.

Real-time data streaming architecture is a potent tool for teams in a range of industries… if you have the right skills and infrastructure.

Real-time data streaming technology drives modern businesses across industries. And its benefits go much deeper than the obvious.

Streaming data from Postgres to Snowflake is a great way to support transactional and analytical workloads. And it’s easier than it seems.

A Postgres to ElasticSearch pipeline is a great way to store and analyze tons of relational data- as long as you keep the two systems in sync.

Picking the right NoSQL database in the Firebase suite can dramatically affect your app’s performance. Here’s how to make the right choice.

How to find your least-painful path to Firestore analytics at scale, whether that’s inside or outside Firebase.

Typical data warehouses are designed to help you effectively manage and analyze data. A real-time data warehouse takes this up a notch.

Syncing data from MySQL to Parquet lets you power both transactional and Big Data workloads. But setting it up effectively isn’t easy.

Getting a reliable real-time data stream out of Salesforce alongside historical data is vital for day-to-day operations and analytics alike, but it can be a challenge. Here’s how we did it.

Data pipelines are everywhere, including some surprising places. Here are some often-overlooked examples that might apply to your industry.

Real-time data is a foundation of modern life that can sometimes hide in plain sight. Here’s how it works and how you can benefit.

There’s been major innovation throughout the entire real-time data landscape over the last few years. These are the major players, and how they fit together.

Syncing your relational database and data warehouse (ie, Postgres and BigQuery) reliably is critical. But it doesn’t have to be hard.

Not all data pipelines platforms are a good fit for every business. In this post, we’ll look at the strengths and weaknesses of top tools.

Want to take advantage of real-time data in your business? This guide will provide in-depth information about real-time data and its uses.

Building a pipeline between Kafka and PostgreSQL has innumerable benefits for your stack. It shouldn’t have to be hard.

Data connectors are critical for bridging the gap between data systems. They come in many forms, and carry many benefits.

Your team can reap the benefits of both Google Sheets and Postgres without sacrificing data integrity across your stack.

Until recently, connecting Google Cloud Firestore to Snowflake hasn’t been easy. Here’s why (and how) to integrate this powerful pair.

You can’t go wrong with Kafka for real-time data, but other platforms give it a run for its money. Here’s how they stack up.

MySQL and Snowflake are both powerful foundations for your data stack – but must be unified around the same data. Here’s how.

Syncing data between MySQL and BigQuery is critical to your operational and analytical data needs – but it’s not always easy to get right.

MySQL and Elasticsearch are a powerful duo to power your app – as long as their data stays in sync. We teach you how to set that up.

Connecting MySQL to Google Sheets is a straightforward process you can complete without writing code. Here are three methods.

Your step-by-step guide to using a real-time data pipeline to migrate from MySQL to PostgreSQL.

All about data pipelines, what makes them automated, and real-world examples.

If you’re looking for Airbyte alternatives, here are our top 7 data integration tools of 2023 to help you choose the best one.

Create a real-time pipeline with a Cloudflare Workers transformation to monitor build times from the GitHub API.

We compare a wide range of features and pricing plan for Airbyte, Meltano, and Estuary to help you find the right solution for your business.

We compare a wide range of features and pricing plan for Airbyte, Fivetran, and Estuaryto help you find the right solution for your business.

In this tutorial, you’ll learn how to integrate Apache Kafka with a real-time GraphQL API using Hasura and Estuary Flow. 

Despite its popularity and widespread use, Fivetran is far from perfect and there are many alternatives in the market with better offerings.

Well-thought-out architecture is what differentiates slow, disorganized, failure-prone data pipelines from efficient, scalable, reliable pipelines that deliver the exact results you want. 

To be successful, a company’s data pipelines must be scalable, organized, usable to the correct stakeholders, and above all, crafted to align with business goals.

Using TypeScript for transformations allows us to prevent many of the common types of pipeline failures that might otherwise slip through the cracks. 

Streaming data pipelines are different from most data pipelines because they handle data continuously — in near-real-time. But they still have the fundamental pieces of a data pipeline.

ETL stands for extract, transform, and load. Together, this set of processes moves data from a source to a destination system.

A data pipeline is a system that takes data from its various sources and funnels it to its destination. It’s one component of an organization’s data infrastructure.

Your data warehouse and data pipelines handle a ton of data every day, and they can quickly make or break your budget. Evaluating exactly how and why you’ll be charged is a crucial planning step.

In this article, we’ll discuss an example workflow that uses Estuary Flow to create a real-time materialized view in Postgres. 

There are only a few core concepts you need to know to start using Estuary Flow. We explain them with illustrations.

Software and data engineering are very different, but you can apply the same best practices to both – if you’re smart about the context.

Your data storage system might be a data swamp masquerading as a data warehouse. Here’s how to tell, and what to do.

If we’re not careful, the modern data stack and ELT can cause new incarnations of problems that have been plaguing us for years.

As we expand our beta program, Estuary is thrilled to announce our partnership with Rockset. 

DataOps is a holistic approach that recognizes the far-reaching impacts data has for business, and addresses common problems on several fronts. 

If you set up your architecture right, you just need one, robust data pipeline system.

It’s vital to avoid data-related power imbalances among individuals, teams, and entire businesses. But data democracy takes work.

The “modern data stack” is a framework used to conceptualize how different data tools work together. But there’s more to it than that.

A comprehensive guide to change data capture in MySQL for cross-functional data teams.

A comprehensive guide to change data capture in PostgreSQL for cross-functional data teams.

As we navigate this rapidly evolving space, we should be familiar with the challenges of change data capture as it currently exists. But we should not expect them to remain the same for long.

Change data capture, or CDC, is the process of recognizing a change in a source data system so that a downstream system can act on that change, usually by updating a target system to reflect new information.

For the past several years, the workforce has been chronically short on data engineers. But what will happen as vendors build services that automate much of their daily work?  

Estuary Flow lets you synchronize data across all the systems you care about, with millisecond latency. What does that actually look like in practice? Here’s a simple — but powerful — example.

Or, how to take an organization-first approach to modern data architecture

The simple concept of materialization can tell us a lot about how (and why) data is stored and represented. In this post: logs and tables; data loading and queries; exports, and more.

Data warehouses like Snowflake and BigQuery are essential to your data stack. Today we’re talking with the two software engineers who built Estuary’s Snowflake and Bigquery connectors to get an inside look at their process. 

Our mission and guiding principles are defined not just by the common challenges of our field, but also by our personal experiences. Here’s how we got to where we are, the beliefs we hold as a result, and the vision we work towards.

We hear these terms used a lot, and to the uninitiated, they can sometimes seem interchangeable. So, what’s the difference between these types of data storage systems?

Being prepared for data scalability challenges and staying aware of best practices can help you avoid common issues.

Apache Kafka is an extremely popular open-source event streaming platform. We talk to Estuary developer Alex about his process and the insights he gained building the Kafka connector.

We believe that an ecosystem of open-source connectors will be critical to the future of data integration. That’s why Estuary is excited to announce our open-source connector repository.

There is a cost associated with putting your data to work, and the benefits you gain depend on the systems you put into place. To maximize net value, you need to strike a balance between minimizing costs and maximizing gain.

Kafka’s challenges have exhausted many an engineer on the path to successful data streaming. What if there was an easier way?

The applications for real-time data processing are diverse and far-reaching. This article highlights just a few.

Real-time and batch are two broad categories of data processing. Though they handle data differently, both are vital to the systems that make our businesses and society run. 

What if we could adapt MapReduce to real-time data processing? Spoiler: it’s awesome and we’re building a next-gen data platform based on it!

At first glance, ETL and ELT seem rigidly defined and mutually exclusive. But to understand our data integration options, we need to look closer.

The term “data pipeline” evokes a mental image that speaks for itself. But their significance for your data goes far beyond that.

When building a product or service, it’s imperative to know that input data will be as expected. If that information comes…

Data, if used properly, is key to success in any organization. It’s impossible to improve without metrics which help us understand…

Sometimes the most relaxing place to travel is nowhere at all.

Design a fitness plan that’s simple and fun and you’ll never miss a workout.

What do you do when geography gets in the way of friendship or love?

You don’t have to be a professional designer to appreciate visual balance and beauty.

Data is exploding and it takes planning to get in front of that trend.

Batch and streaming usually require different architectures and building infrastructure, but shouldn’t.