In today’s digital age, robust and faster data analytics is essential for your organization’s growth and success. The faster you deliver analytics-ready data to your analyst, the faster they can analyze and derive insights.
Though you would have adopted the ELT process with EL data pipelines to load data quickly to the warehouse, your team would still face inefficient and delayed analysis. Because of the ‘T’ part of ELT, i.e., Data Transformation, like joining, aggregating, and computing data, for analysis post-loading it to a warehouse.
Data transformation is usually complex and time-consuming. It becomes a bottleneck for your analyst to deliver faster insights. They are dependent on data engineers to manually create transformation workflows on top of your data warehouse, or they would have to use another third-party tool to transform the data.
It gets frustrating for them as they have to wait to access analytics-ready data and start deriving insights.
At Hevo, we believe that your data pipeline and transformation should be tightly integrated on a single interface to deliver a complete ELT workflow for robust and efficient analysis.
Thus, Hevo provides a complete ELT data analytics pipeline with in-built data transformation. Your data teams or data analysts can query the data on the warehouse using Hevo and make it analytics-ready.
Deliver analytics-ready data faster to your analysts using Hevo
Hevo offers your data team a unified interface to load and transform your data at the warehouse. Thus, enabling efficient and quick analysis and closing the gap between your data engineers and data analysts.
Hevo’s in-built data transformation equips your data team to quickly deliver analytics-ready data by automating the complete ELT workflow of loading data to the warehouse from multiple sources and preparing it for analysis.
Using our transformation dashboard, your data team can directly work and run queries on the warehouse data through Hevo. They can automatically kick-off transformation as soon as data arrives in the warehouse and make it analytics-ready.
Once the data is analytics-ready, they can export the output tables to a BI tool or directly deliver it to analysts. Your data analysts have continuous access to fresh analytics-ready data.
It’s a super easy interface to model your data at any warehouse
Transforming data at the warehouse on Hevo is super easy. Anyone from your data team with SQL competence can set up a pipeline and transform the data for analysis.
Hevo provides an easy-to-use and interactive interface with SQL capabilities to transform the data on any warehouse you use.
Your team can write queries for data at your warehouse on Hevo to transform that data; every transformation unit or query is called Model on Hevo.
They can combine a series of models and build a Transformation Workflow. Using Workflow, they can join multiple models, manage dependencies between them, and sequence the models accordingly.
Hevo offers a highly intuitive DAG (Directed Acyclic Graph) interface to orchestrate your Workflow. It helps your team seamlessly design the transformation workflow to generate the final required data model.
Depending on your use case or pipeline frequency, you can schedule your transformation models and workflows to run at a specific time and frequency.
The complete analytics workflow can be easily deployed in minutes, generating fast and highly effective analysis.
You can run any level of complex queries on your warehouse using Hevo
Hevo’s transformation interface enables your team to perform complex transformation models and workflows at your warehouse.
Following are the major types of transformations our customers employ using Hevo,
1. Data Cleansing
Data quality is essential for an effective analysis. Your data should be fit to perform analytics and generate accurate reports. The data you get from your sources could contain a few anomalies, or there could be bad data in the dataset.
Also, the source data may not usually per your organization’s nomenclature. Transforming data to your nomenclature and removing all the bad data is data cleansing.
Using Hevo’s data transformation, your data team can ensure your data is of good quality for effective analysis and accurate intelligence.
2. Combining Data
Your analyst prefers a combined view of data from multiple sources to perform in-depth analysis and derive effective insights. E.g., generating a deeper view of customers by combining your CRM data like Salesforce with your payment gateway data like Stripe.
Using Hevo, you can combine the data from various sources at the warehouse.
3. Aggregating Data
It would be best to calculate all your critical metrics or KPIs at the warehouse only, and then push to the BI tool for analysis.
You can write SQL queries to aggregate data and generate your key business metrics at the warehouse on Hevo’s interface. A data analyst can later filter these metrics on different dimensions in the respective BI tools.
Let’s understand how Hevo’s in-built transformation elevates your analysis
Your analyst could be demanding a 360-degree view of customers, i.e., combining data recorded on your customers from various touch points into a single dataset.
Your data team can quickly build the unified customer view using Hevo’s in-built data transformation. They can create a data pipeline for each source, i.e., your CRM tool like Salesforce, customer support tool like Zendesk, product usage tool like Amplitude, payment gateway like Stripe, etc.
Once data is loaded into the warehouse, they can easily automate a transformation workflow to join the data and create a unified view.
The complete process of building and updating 360-degree customer views is easily automated using Hevo by streamlining your loading and transformation in a single place.
Get the flexibility to output your transformed data
The output from each transformation model can be loaded either to a new table or existing table in the warehouse.
There are 2 different ways to output your transformed data to a table using Hevo. You can choose any one of them depending on your use case.
- Full Mode – In full mode, the complete data is uploaded to the table whenever a model or workflow runs. Every time, the table is deleted and re-created with complete data. This allows you to change the query without worrying about the changed table schema.
- Incremental Mode – Incremental Models allow you to export only the changed data to the output table. Instead of recreating the table, you can choose only to update existing records and insert new ones into the table.
Get a centralized data workspace with Hevo
Hevo’s in-built data transformation with no-code data pipelines and Reverse ETL helps your team manage the complete data flow for analysis from Hevo itself. They can load data to the warehouse, run queries on the stored data at the warehouse from Hevo, and push the post analytics data back to business applications.
Our customers have benefited immensely from Hevo’s in-built transformation capabilities
Getting Started with Hevo
Power up your analytics engine by automating the complete ETL workflow of loading data to the warehouse and transforming it for analytics. Sign up for our 14-day free trial and experience the ease of preparing data for analysis with Hevo.