5 Possible Data Integration Platforms and Solutions

5 Possible Data Integration Platforms and Solutions

You have heard about all the amazing new tools for data analytics, business intelligence or machine learning. But one very important aspect for data strategies is often overlooked and neglected - the data logistics part. Meaning the question of how to integrate and move your existing data, breaking down data silos and making those lofty big data dreams actually happen. Many approaches are possible, so let's examine them for you and your needs.

1. Crontab

In the realm of big data, this widely known tool is actually not a good choice. It's a simple solution that everybody knows that can be enough for some kind of scheduled tasks. But data pipelines are far too complex and have too many operational issues after a certain scale. Therefore, we would not recommend this one.

Advantages:

  • Widely available
  • Simple
  • Known

Issues:

  • No native monitoring / overview
  • No re-schedule / replay functionality when errors occur
  • No coding framework: wildly varying scripts
  • Hard to control execution environment / installed libraries

2. Cloud-Specific (e.g. Azure Data Factory)

Large-scale cloud providers like Amazon, Google and Microsoft have their own solutions for data integration. They vary in being code / no-code solutions and in being purely proprietary or adjusted open-source software. An example would be Azure Data Factory. These tools can be a good fit if your are already fully invested in a specific cloud or vendor and don't mind lock-ins.

Advantages:

  • Fully-optimized for specific cloud
  • Large-scale companies with lots of ressources
  • Usually good integration into cloud specific tools

Issues:

  • Lock-In effects: It's hard to change your cloud after you have built on top of these solutions. In most cases even modified open-source tools are integrated in a way that still keeps you locked-in with specific idiosyncrasies.
  • Sometimes vendor-specific languages and syntax to write code (if code-based)

3. No-Code (e.g. Fivetran)

There are many companies offering proprietary solutions with no-code data pipelines. They are great if you use standardized and well-known API's like Google Analytics, Shopify, ... and don't have advanced requirements. An example is Fivetran.

Advantages:

  • Simple
  • No or few developers required
  • Systematic framework

Issues:

  • Hard to customize: possibility to run into limitations
  • Impossible to extend: everything you need has to be supported by the platform

4. Code-Based (e.g. Apache Airflow)

Code-based data integration tools give you the ability to build anything you need in a systematic framework. This means full customization options while providing monitoring, re-scheduling and pre-built connectors for common data sources. A system like this is best for companies with slightly advanced requirements and environments. The most popular data workflow and data engineering system is Apache Airflow, originally released by AirBnb. For the growing field of data engineering, this has been the go-to tool for some time now.

Advantages:

  • Python-based tasks, all available libraries can be used
  • Pre-built connectors for popular databases, API's, ...
  • Full customization ability
  • No-Lockins
  • Large community
  • Systematic framework for code structure
  • Monitoring, Re-scheduling

Issues:

  • Hard to operate when self-hosted, many moving parts
  • Hard to scale, not natively integrated
  • Needs developers, meant for data engineering

5. Fully Managed Open-Source (e.g. Airlaunch)

Using a tool like Apache Airflow as a Managed Service has one big advantage. All the complexity of operating and scaling the system goes away, which often is the reason for data strategies to fail. Too much emphasis on operations issues and not enough on realizing results drains ressources. Companies like ours (Airlaunch) solve this by offering non-modified open-source tools in combination with a fully managed service. We also added additional features, like a local worker solution that can run tasks on edge nodes. This might be helpful in IOT or other similar use cases.

Advantages:

  • All the advantages of Apache Airflow
  • No operations issues
  • No scaling issues
  • Lower operations costs
  • Added ability to run local workers

Issues:

  • Still needs developers (We can help you with that as well :))

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