The ETL connectors you can't find anywhere

Cloud ETL tools are primarily used by analytics engineers, data analysts, and data engineering teams. It’s not uncommon for data scientists, business operations teams, or marketing teams to leverage cloud ETL tools for more niche use cases. One of the biggest benefits of a cloud ETL solution is the ease of use. The real question should be ‘Who shouldn’t use a cloud ETL tool?’

Cloud ETL tools like Portable offer simple, off-the-shelf data pipelines that you can set up in minutes instead of days. By connecting to over 300+ data sources, you can centralize source data from disparate business applications into your cloud data warehouse (Snowflake, BigQuery, Redshift, PostgreSQL, etc.) for analytics, automation, or product development.

Yes, cloud data integration tools are cost-effective. They make data replication simple, and they significantly reduce the development and maintenance of custom code. If you build in-house, you’ll be spending cycles writing Python code, deploying infrastructure and cloud services, and worrying about how best to optimize your cloud compute spend. Cloud ETL tools save you the hassle. Because pre-built templates can be reused across clients, cloud ETL solutions can amortize the cost of connecting to various sources across many clients instead of paying to manage custom one-off workflows.

Most data teams use a cloud ETL tool to sync data into their data warehouse for 1) data analysis, 2) process automation, or 3) product development. In all three scenarios, a cloud integration platform offers a seamless ingestion layer so you can focus on data transformation and data quality instead of the ETL process. Some teams even leverage the scalability of cloud ETL tools for ingestion into machine learning models for advanced analytics in addition to more common business intelligence use cases.

Portable offers no code connectors to over 300+ data sources (CRM systems, ticketing systems, email service providers, etc.). With simple-to-understand pricing and a data processing engine built for petabyte-scale workflows, you can be confident that Portable can extract, transform, and load the datasets you need into your warehouse for data analytics.

Portable supports the most commonly used data warehouses and databases. This includes Snowflake, Google BigQuery, Amazon Redshift, PostgreSQL, and MySQL. We have other data warehousing solutions and data lakes on our roadmap like SQL Server, Google Sheets, and Databricks to help teams unlock more seamless data management wherever their data processing takes place.

ETL pipelines and the ETL process involve a significant amount of data transfer logic. In addition to abstracting away the need for infrastructure, a cloud ETL product handles all data extraction (making API calls, processing JSON or XML responses, handling errors and rate limits), in-flight data transformation (defining data types, creating schemas, ensuring join keys exist), and data loading (creating DDLs to load data, ensuring unstructured data is organized for your downstream data needs).

Portable does not currently provide an open-source solution for ETL. We believe data teams should spend their time building insights, automating workflows, and building products instead of developing data pipelines or maintaining workflows. With Portable, teams no longer need to deploy infrastructure, read API documentation, or maintain long-tail API connectors.

As a data integration tool, Portable can move data in near real-time. In most scenarios, we are simply limited by the constraints of the upstream systems we are extracting data from. When you set an ELT data flow to run on a cadence, you can schedule the flow every 15 minutes, every hour, every 6 hours, every 12 hours, or every day. If you want to run the data flow manually, it’s free!

Nowadays, ETL and ELT are used interchangeably in conversation in most scenarios. Technically, Portable enables an ELT architecture (extracting data from APIs, loading the data into your cloud warehouse, and allowing your team to transform the data with SQL after it has been loaded). That being said, all ETL and ELT tools transform data in some capacity while it is in transit to your processing environment. At Portable, we do not aggregate data or create data models while data is in motion, we focus our transformation efforts on simply schematizing data so it’s easier to query for analytics.

The best extract, transform, load (ETL) tools are: Portable for high-performance custom ETL integrations. Fivetran for change data capture from database sources (PostgreSQL, MySQL, etc.) and ETL from common SaaS applications like Salesforce or Oracle that store big data volumes. On-premises tools like Informatica, Matillion, and Talend for enterprise use cases where you need to deploy a solution on-premises or within your cloud environment (Microsoft Azure, Google Cloud, AWS, etc.).

We always recommend focusing on business value instead of technologies. That being said, technology can provide significant leverage to your team as you create business value from data. In addition to a cloud-based ETL solution, many teams leverage a cloud warehouse and a data visualization tool as the core data stack. From there, a data transformation solution can help organize and version your queries, a Reverse ETL platform can help automate workflows, and behavioral data platforms can make it easy to create new data from your website or mobile app. If your business has requirements for real-time data processing, it could be worth checking out stream processing platforms as well.