4 simple steps for migrating to
Google BigQuery with zero coding

Create connections
between data
sources and Google BigQuery

Prepare a source to the Google BigQuery pipeline by selecting tables in bulk

Assemble a workflow and schedule it to start the Google BigQuery migration process

Share your data
with third-party platforms
over API Hub

Simple and Intuitive

Switch to Google BigQuery like a boss

High-Speed Performance

Enjoy Google BigQuery high-speed performance with codeless data environment

Prebuilt Transformation

Say goodbye to tedious manual tasks with prebuilt transformation templates

Monitoring Data

Monitor your Google BigQuery data frequently

Hear how Lyftrondata helped accelerate the data
journey of MOL Group

New applications

Lyftrondata enables instant analytics on WNI weather data that helped us streamline shipping lines.

Koichi Tsuji

Consulting Partner at MOL Group


Google BigQuery Integration is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service that supports querying using ANSI SQL. It also has built-in machine learning capabilities.

Integrations: Hadoop Integration is possible with Google BigQuery Integration tool.
Platform: Machine Scaling is feasible with Google BigQuery Driver.
Processing: Cloud Processing is accessible with the Google BigQuery Connectors tool.
Data Transformation: Real-Time Analytics is possible with BigQuery ETL.

Inappropriate for SMBs: Google BigQuery Integration is less appropriate to use in small businesses where data volume is low

Incapable IT resources: IT resources are not enough to maintain data quality with Google BigQuery Connectors tool.

Relational database issues: BigQuery ETL cannot be used to substitute a relational database.

Query running issues: Google BigQuery Driver is oriented on running analytical queries, not for simple CRUD operations and queries.

Start modernizing your Spark journey today