An end-to-end series of digital procedures used to gather, change, and send data is known as a data pipeline.
Data pipeline tools are a type of software that allows huge amounts of data to be transported from multiple sources to a single location, usually a data warehouse.
Data is often “normalized” or “transformed” in the data warehouse so that it has a standard structure and schema and can be used for analysis and reports.
Data pipelines are classified according to how they’re used. The two most popular pipeline types are batch processing and real-time processing.
Batch Processing Data Pipeline
A batch process is generally used for classic analytics use cases, such as business functions and traditional business intelligence, where data is collected, converted, and transported to a cloud data warehouse on a regular basis.
With minimal human intervention, users may swiftly mobilize high-volume data from siloed sources into a cloud data lake or data warehouse and schedule jobs to process it. Batch processing allows users to collect and store data within a batch window, which aids in the efficient management of huge amounts of data and repeated operations.
Real-time Processing DataPipelines
Using a high-throughput messaging system, streaming data pipelines allow users to ingest structured and unstructured data from a variety of streaming sources such as the Internet of Things (IoT), linked devices, social media feeds, sensor data, and mobile applications.
To enable real-time analytics for use cases like fraud detection, predictive maintenance, targeted marketing campaigns, or proactive customer support, data transformation happens in real-time utilizing a streaming processing engine like Spark streaming.
On-Premises vs. Cloud Data Pipelines
Organizations have traditionally relied on data pipelines created by in-house developers. However, given the quick speed of change in today’s data technology, developers are frequently forced to rewrite or create new code in order to keep up. This is both time-consuming and expensive.
Building a dependable cloud-native data pipeline allows businesses to quickly migrate their data and analytics infrastructure to the cloud and expedite their digital transformation.
Companies can build and manage workloads more efficiently by deploying a data pipeline in the cloud. Control costs by scaling up and down resources based on the amount of data being processed.
Organizations may increase data quality, connect to a variety of data sources, ingest structured and unstructured data into a cloud data lake, data warehouse, or data lakehouse, and manage complicated multi-cloud settings by using a cloud data lake, data warehouse, or data lakehouse.
To promote innovation and create a competitive edge for their enterprises, data scientists and data engineers want dependable data pipelines to access high-quality, trustworthy data for their cloud analytics and AI/ML initiatives.
There are many different sorts of data pipeline tools available, and we will be focusing on one of them called Tapclick in this article.
We’ll start by going through our data pipeline solution, TapClicks, which helps sales and marketing teams consolidate, store, and analyze data. We go over the four qualities you should look for in a marketing data pipeline solution, as well as how TapClicks does them:
- Obtaining information from all of your marketing channels, as well as any other data source.
- All of your data is stored in a properly controlled data warehouse that can be accessed by any marketer (with no coding required).
- Within the TapClicks platform, you can set up transforms and complex calculations once.
- Creating and distributing automatic and scheduled visualizations and reports for clients.