The advent of cloud data warehousing has led to companies around the world investing heavily in data management and advanced analytics.
But, there's a problem.
Software developers are in short supply and data integration consultants are expensive.
As a result, business intelligence teams are looking for no-code solutions that empower data analysts and business stakeholders to harness the power of data without dependence on costly engineering talent.
No code ETL (extract, transform, load) is one of the easiest ways to provide leverage to your data analytics with tooling instead of headcount.
By simply configuring credentials for data sources and your data warehouse, no code ETL / ELT solutions can move data in near real-time from hundreds of data sources to your analytics environment.
Here's how you get started with a no-code ETL pipeline using Portable.
Create an account (with no credit card necessary)
Authenticate with your data source
Select a data warehouse and configure your credentials
Connect your data source to your analytics environment
Run the flow to start replicating data from your source to your warehouse
Use the dropdown menu to set your data flow to run on a cadence
There are 3 ways to develop an ETL pipeline: 1) code, 2) low-code, and 3) no-code.
Code - Historically, teams had to build their data pipelines. They would hire ETL developers or data engineers who would deploy infrastructure and use programming languages (python, java, etc.) to write custom logic. While open-source frameworks could help accelerate development, you still needed to write code.
Low-Code - As more data integration companies appeared, it's more common for non-technical users to leverage drag-and-drop solutions to create integrations and data pipelines. This approach is more accessible because it doesn't require engineering effort, however, custom logic still needs to be created in a one-off manner.
No-Code - Whenever possible, no-code api connector solutions offer the simplest ETL pipeline. Instead of writing logic from scratch each time, no-code solutions simply involve configuration. If you want a workflow that moves data from a source to a destination, you simply enter your credentials for the source and destination and start moving data.
Let's dig deeper into no-code data pipelines and the benefits of using a SaaS platform for ETL.
With a no-code ETL solution, a user can configure a recurring data extraction that loads information into a data warehouse for analytics, all with no code.
No code pipelines incorporate prebuilt logic that extracts and loads data. They replace the need for scripting logic in programming languages like Python or Java to move data.
No code ETL pipelines allow data teams to quickly and easily:
1. Create business value
2. Access information from disparate data sources
3. Abstract away ETL complexity
Instead of writing custom logic each time you need a data integration, a no-code solution turns code into configuration, reducing implementation time to minutes instead of days.
When it comes to value creation, it's common to focus on creating value through 3 use cases:
Analytics - The goal is to organize all of your data into a centralized location to power insights and dashboards. Business leaders need data at their fingertips to make better strategic decisions.
Process automation - The goal is to save time by automating manual tasks and business processes. Instead of manually copying information from one system to another, it should take place automatically.
Product development - The goal is to turn information into valuable data products that customers can purchase. These could be insights, automated workflows, or raw data feeds for monetization.
Whether you're focused on analytics, automation, or product development, a no-code solution can make data accessible in your downstream systems quickly.
Instead of using Excel spreadsheets that have to be manually updated each time you run a report, a cloud data integration solution can manage the entire workflow.
With a no-code solution in place, your team can focus on data modeling and data transformation instead of pipeline logic. While this doesn't guarantee perfect data quality, it allows your team to focus on what matters instead of scripting custom logic to access the data you need.
There are 3 main stages in the ETL process that a no-code ETL solution can abstract away:
Data extraction - Instead of reading API documentation, parsing JSON responses, handling errors, and managing the handoff of data along the way, a no-code ETL solution can handle all data extraction on your behalf.
Transformation - There are two types of transformation: 1) data transformation while information is in motion (i.e. field additions, calculations, etc.) and 2) data modeling once the data has hit your destination (AWS Redshift, Microsoft Azure Synapse, etc.). Either way, a no-code solution allows you to get data loaded so you can focus on the transformations that truly add value.
Data loading - If you can't load data, the rest of your data pipeline is worthless. No code ETL tools help with the validation of schemas and delivery of data to common destinations (Snowflake, MySQL, SQL Server), so you get the data you need at your fingertips.
Portable is the best ETL tool. (We have to say it). But honestly, it depends on your specific needs - different ETL tools can make sense if different scenarios.
Here are a few of the reasons to pick one no-code ETL / ELT solution over another:
SaaS vs. open source - If you're looking for a no-code solution, you are likely looking for a software-as-a-service (SaaS) solution that makes the whole setup process a breeze. On the other hand, if your team needs direct access to code for security, regularity, or extensibility purposes, it can make sense to evaluate open-source ETL solutions like Apache Nifi or Singer.io.
On-premises vs. cloud-based - Whether you use a no-code, low-code, or scripted solution, there are different types of deployment models you can choose from as well. Many enterprise solutions (Informatica, SSIS, Talend) are less intuitive to set up but can be deployed in your cloud environment or on-premises data center. While it can be important for large organizations, most companies prefer to leverage a cloud-based SaaS solution for data integrations in order to get a user-friendly experience with scalability out-of-the-box.
Pricing - Pricing models in the ETL ecosystem can be complicated to say the least - especially when you're dealing with big data sets. It's not uncommon for vendors to price using a proprietary volume metric that changes day-over-day or month-over-month. This can be difficult to understand, frustrating to mitigate, and impossible to forecast. At Portable, we keep things simple by charging on active data flows. It's a simple fixed fee for each recurring data sync you set up.
Here are data integration companies that offer no code ETL solutions:
Data integration platforms typically force clients to develop and maintain their code for custom connectors to bespoke applications; however, Portable is changing this. With a no-code solution for custom ETL connectors, you can finally pull data from bespoke applications into your data warehouse with zero code.
Most ETL tools focus on the biggest data sources - On-premises systems like Oracle or SAP. File sources like CSVs sitting in Amazon S3 or Azure Blog Storage, and the largest SaaS applications like Salesforce or Workday.
While this is important functionality to consider when initially standing up your data stack, over time you will need a solution that helps pull data from your hard-to-find applications.
With Portable, you can extract data seamlessly from bespoke applications and load the datasets into your analytics environment with no code.
It's simple, easy, and only takes 5 minutes to get started.
Need an ETL platform?
Want to focus on data analysis instead of infrastructure?