200X Acceleration at
1/10th of the cost
Zero
maintenance
No credit card
required
Zero coding
infrastructure
Multi-level
security
Simplify IBM CloudSQL integration in
4 simple steps
Create connections
between IBM CloudSQL and targets.
Prepare pipeline
between IBM CloudSQL and targets by selecting tables in bulk.
Create a workflow
and schedule it to kickstart the migration.
Share your data
with third-party platforms over API Hub
Why choose Lyftrondata for IBM CloudSQL Integration?
Simplicity
Build your IBM CloudSQL pipeline and experience unparalleled data performance with zero training.
Robust Security
Load your IBM CloudSQL data to targets with end-to-end encryption and security.
Accelerated ROI
Rely on the cost-effective environment to ensure your drive maximum ROI.
Customer's Metrics
Track the engagement of your customers across different channels like email, website, chat, and more.
Improved Productivity
Measure the performance of your team and highlight areas of improvement.
360-degree Customer View
Join different data touch points and deliver personalized customer experience.
Hassle-free IBM CloudSQL integration to the platforms of your choice
Migrate your IBM CloudSQL data to the leading cloud data warehouses, BI tools, databases or Machine Learning platforms without writing any code.
Hear how Lyftrondata helped accelerate the data journey of our customers
FAQs
What is IBM CloudSQL?
IBM Cloud SQL Query is a service within the IBM Cloud ecosystem designed to run SQL queries on data stored in IBM Cloud Object Storage. It allows users to interact with their object storage data using familiar SQL syntax, enabling easy data analysis, querying, and processing without needing to move the data to a database or another environment.
Cloud SQL Query is particularly useful for large datasets, providing a way to extract, transform, and analyze the data without having to load it into a structured database. It is serverless, meaning users do not need to manage the infrastructure themselves.
What are the features of IBM CloudSQL?
SQL-Based Queries:
Supports SQL syntax for querying data stored in IBM Cloud Object Storage, making it easy for users familiar with SQL to analyze large data sets.
Scalability:
Automatically scales to handle large datasets or complex queries, adjusting resources as needed based on query demands.
Support for Various Data Formats:
Works with various data formats such as CSV, JSON, Parquet, and ORC, providing flexibility for users working with different types of datasets.
What are the shortcomings of IBM CloudSQL?
Performance for Complex Queries:
Limited Optimization: Since Cloud SQL Query runs directly on data stored in IBM Cloud Object Storage, performance can be slower for complex queries, especially those involving multiple joins or large-scale aggregations. Supports SQL syntax for querying data stored in IBM Cloud Object Storage, making it easy for users familiar with SQL to analyze large data sets.
No Indexing: Unlike traditional databases, data in object storage is not indexed, which can result in slower query execution times for large datasets or complex filters.
Limited SQL Functionality:
Basic SQL Support: While it supports standard SQL queries, some advanced SQL functions may not be fully supported. Users looking for advanced analytics or complex transaction capabilities may find the functionality somewhat limited.
No Transactions: Cloud SQL Query is not designed for transactional operations (ACID compliance), making it unsuitable for use cases that require robust data integrity management.
No Data Caching or Partitioning:
Lack of Caching: Cloud SQL Query doesn't offer built-in data caching, so frequently running the same query on large datasets can result in slower performance compared to services that allow for data caching or materialized views.
No Data Partitioning: There is no support for automatic data partitioning, which could be useful for improving performance when querying very large datasets.