Author: John Lafleur

MongoDB is a distributed database that is built for modern transactional and analytical applications and may be used for rapidly changing, multi-structured data. On the other hand, PostgreSQL is an SQL database that has all of the features that you require from a relational database. If you are unsure of the differences between these systems, on the MongoDB website, you can find an article that compares PostgreSQL and MongoDB.

Choosing one or the other between MongoDB and PostgreSQL may not be your only option – in fact, because each database has different strengths, you may wish to use them side-by-side. If this is your case, then you may need to sync data between them.  

This article delves into the integration of Airbyte with some of the most popular data orchestrators in the industry – Apache Airflow, Dagster, and Prefect. We'll not only guide you through the process of integrating Airbyte with these orchestrators but also provide a comparative insight into how each one can uniquely enhance your data workflows.

We also provide links to working code examples for each of these integrations. These resources are designed for quick deployment, allowing you to seamlessly integrate Airbyte with your orchestrator of choice.

There are some great guides out there on how to create long-term memory for AI applications using embedding-based vector stores like ChromaDB or Pinecone. These vector stores are well-suited for storing unstructured text data. But what if you want to query data that’s already in a SQL database - or what if you have tabular data that doesn’t make sense to write into a dedicated vector store? 

For example, what if we want to ask arbitrary historical questions about how many GitHub issues have been created in the Airbyte repo, how many PRs have been merged, and who was the most active contributor overall time? Pre-calculated embeddings would not be able to answer these questions, since they rely upon aggregations that are dynamic and whose answers are changing constantly. It would be nearly impossible - and efficient - to try to answer these questions with pre-formed text documents and vector-based document retrieval.

The recent progress in AI is very exciting. People are using it in all sorts of novel ways, from improving customer support experiences and writing and running code to making new music and even accelerating medical imaging technology. 

But in the process, a worrying trend has emerged: the AI community seems to be reinventing the data movement (aka ELT). Whether they call them connectors, extractors, integrations, document loaders, or something else, people are writing the same code to extract data out of the same APIs, document formats, and databases and then load them into vector DBs or indices for their LLMs. 

Open-source technology is becoming increasingly popular in the data integration industry, and for good reasons. Open source creates the right incentives, allowing users to own their data entirely, unlike closed source, where you build knowledge in a proprietary tool with a price tag. Open source also creates communities around common problems, allowing for the exchange of valuable knowledge and collaborative problem-solving. 

In this article, we will start investigating the reasons behind the adoption success of open source before delving deeper into the data integration industry, more specifically focusing on open-source vs. closed-source ELT (Extract, Load, Transform) solutions. We will discuss how open-source ELT allows for greater control over the data integration process, more efficient data processing, and cost savings for organizations. Additionally, we will explore the growing trend of open-source ELT adoption in the industry and examine the future of open-source data integration. 

A semantic layer is something we use every day. We build dashboards with yearly and monthly aggregations. We design dimensions for drilling down reports by region, product, or whatever metrics we are interested in. What has changed is that we no longer use a singular business intelligence tool; different teams use different visualizations (BI, notebooks, and embedded analytics). 

Instead of re-creating siloed metrics in each app, we want to define them once, open in a version-controlled way and sync them into each visualization tool. That’s what the semantic layer does, primarily defined as YAML. Additionally, the semantic layer adds powerful layers such as APIs, caching, access control, data modeling, and metrics layer.