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Organizations are aspiring to become data driven. Intuitive decision-making is getting replaced by fact-based decision-making that is backed by data. Often, enterprises find it difficult to implement this data-driven decision-making as most of the workforce performing this task could be non-technical. Integrating data to make it accessible for analytics is a highly technical process. This is what data pipelines enable organizations to do; making data analysis easier for business analysts.
Pipelines in Hevo
A data pipeline, or Pipeline in Hevo is a no-code data processing framework that loads data from any Source such as a database, SaaS application, or file into a Destination database or data warehouse. For example, you may load data from your Facebook Ads account to a Google BigQuery data warehouse for analysis.
With just a few clicks you can have analysis-ready data at your finger tip, without any data loss. You can even view samples of incoming data in real-time as it loads from your Source into your Destination.
The following form the key components of a Pipeline:
Source: A Source is a database, an API endpoint or a file storage that holds the data that you want to analyze. Hevo integrates with over 100+ Sources. Read Sources.
Transformations: Python or UI-based Transformations are useful when you want to clean, enrich, or transform your data before loading it to your Destination. Read Transformations.
Schema Mapper: The Schema Mapper lets you map your Source schemas to tables in your Destination. Read Schema Mapper.
Destination: The Destination is the data warehouse or database where the data collected from Sources is stored. Read Destinations.
In a Pipeline, one Source maps to one Destination only. However, the same database or data warehouse may be configured as a Destination for multiple Sources.
Benefits of Creating Pipeline Using Hevo
Following are some of the benefits of creating Pipeline using Hevo:
Near Real-Time Data Transfer: Hevo provides real-time data migration, so you can perform data analysis anytime as per your business need.
100% Complete and Accurate Data Transfer: Your Hevo Pipeline ensures reliable data transfer with zero data loss.
Scalable Infrastructure: Seamless integrations with Sources can help you scale your data infrastructure as per your business need.
Live Monitoring: Options to view the data and its movement along different stages of the Pipeline allows you to monitor the progress of your data replication. Read Data Ingestion Statuses and Viewing Pipeline Progress.
Connectors: Hevo supports integration with various Destinations including Google BigQuery, Amazon Redshift, Snowflake data warehouses; Amazon S3 data lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few. Read Sources. Read Sources.
Transformations: Pipeline created in Hevo provides preload transformations. You can also use the Python or the drag and drop transformations like Date and Control Functions, JSON and Event Manipulation to perform your own transformations. These can be configured and tested before putting them to use. Read Transformations. Transformations.
Schema Management: Pipeline created in Hevo takes away the tedious task of mapping and managing the Destination schema and automatically schema management & automatically detects the schema of incoming data and maps it to the destination schema. Read Schema Mapper. Read Schema Mapper.
Refer to the following table for the list of key updates made to this page:
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