What is Stitch and Why Use It for Data Pipelines?
- What Is Stitch?
- What Are Data Pipelines
- Why Do We Use Data Pipelines
- Where Do Data Pipelines Fit Into Your Data Infrastructure
- Stitch vs. Fivetran
- Stitch vs. Airflow
If you haven't yet heard about Stitch — this ETL (extract, transform, and load) is one of the best solutions for creating a data pipeline and connecting with other apps and data storage services.
Stitch might be the perfect solution for many companies because it offers a fast, easy setup while offering plenty of security features. This is a cloud-based option that makes an excellent fit for many and provides high ROI.
Consider the following details about Stitch and how it compares with other similar options out on the market today.
What Is Stitch?
Stitch is an ETL service that uses the cloud to transfer data via an open-source platform rapidly. One of the outstanding features of Stitch is that it connects with many common data sources like SaaS apps, Salesforce, and Mongo DB. This technology creates a data pipeline with many features and potential.
Once the data pipeline is created, it can send data to a destination like Amazon Redshift. Stitch also allows users to replicate data from sources such as Amazon S3 and MySQL.
What Are Data Pipelines?
A data pipeline is a means of transferring data from one location to a destination. The information retrieved comes from databases, SaaS, an API call, and more, and the end destination can be anything from a data lake to analytics for use by companies.
The data extracted from the source is transformed and loaded to the end-user interface inside the data pipeline. However, there are several ways data pipelines handle data.
ETL refers to transferring data from a data warehouse like Amazon Redshift through a data pipeline. It stands for Extract, Transform, and Load. This means the data is pulled from the source (transaction database), transformed into a useable format, and loaded to the user interface, app, or data warehouse.
One of the significant benefits of this method is one language is used across the board for data scientists, BI teams, and anyone using the information. Another prominent feature is the accuracy of the data stored and transferred. The data stays the same at all stages, is scalable, and provides a lower instance of latency.
ELT is used primarily with data lakes. It means the information is loaded directly into the data storage and pulled in its stored form, and transformed into usable formats on the destination end.
The significant benefits of using this method for transferring and storing data include its cost-effectiveness and flexibility when specific formats are needed for the destination. The raw data is transformable at any point and doesn't require the extra step in the data pipeline.
EL stands for extract and load. This method is the correct type of data pipeline selection if the data is clean and accurate with no aggregation.
Use this method for batch loading for historical data or even log files. Extract and then load is also good for scheduled queries. It is a simple way to move data that is all ready for analysis or viewing.
Why Do We Use Data Pipelines?
Data pipelines make it possible to store large amounts of data that transfer to various apps and software. The old methods of using data silos or other means make it difficult to find the necessary information. They also leave room for errors and other instances of latency and redundancy.
Where Do Data Pipelines Fit Into Your Data Infrastructure?
Data pipelines can fit into infrastructure when they have instances of aggregating data for use cases and even product improvements. It's also ideal for those who have an e-commerce platform and need various reporting options with the ability to consolidate collected data.
Remember that a data pipeline is usually a good option for a growing or larger company. It might also prove effective for instances where there's a team of data scientists or developers in need of storage and data transfer. It's also great for companies with significant cash flow or that manage money for customers or entities.
To get started, choose the method that works, whether it's ELT or ETL, and find a provider with the right features and services to match your business needs. The implementation is seamless, and you can migrate your data to its storage point. Building a data framework is simple with the right tools and services.
A few competitors are offering similar services for ETL. However, each has its unique features and isn't the same experience. It's essential to know how each works and compares to Stitch.
Stitch Vs. Fivetran
Stitch is a cloud-based ETL filled with various integrations and syncs. It's ideal for small businesses looking for a better way to use data for all their needs. It's also suitable for developers and primarily uses JSON. There are additional security protocols and various other options available for Stitch. This method uses raw rows rather than active rows.
Fivetran is also a cloud-based ETL and allows you to extract data for use with analytics and other managed service providers like Salesforce. It's an excellent choice for data analysts and features the ability to link to a data storage option. This option uses active rows and has options for more security.
Stitch Vs. Airflow
Apache Airflow is an ideal ETL process for developers and is also a cloud-based option. Airflow allows the user to schedule and monitor workflows with directed acrylic graphs. With this selection, the developers can transform the data by using Python.
In contrast, Stitch can transform data to be compatible with the data destination. This option works with a data warehouse of engines like Spark. Various methods define the transformations, such as Java or even a graphical user interface.
Stitch is an ideal choice for ETL needs when data is transformed for a specific destination. It is also great for a small business or uses with developer projects. Stitch has plenty of data solutions and integrations packed with flexible features that make it valuable to those working with various data sets.