DATA ENGINEERING TOOLS
TO BUILD MASSIVELY SCALABLE DATA PIPELINES
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Efficiently manage data pipelines with the flexibility of preferred programming language and data processing frameworks.
Explore, build, and deliver data pipelines with ease. Avoid the typical bottlenecks of data ingestion and preparation with a single platform that meets all of your big data engineering requirements. Get intelligent data preparation to support the data needs of all users.
EXPLORE DATA PIPELINES
Configure Data Source Access
Connect and explore data from a variety of relational and non-traditional databases. Conduct data exploration on unstructured data sets residing on AWS S3, Microsoft Azure Storage, or Google Cloud.
Explore Data With Ease
Have a single view of all of your data sources with a single metastore— structured and unstructured — and query any data source using your preferred tool, Qubole notebooks, ANSI SQL, or via API calls.
BUILD DATA PIPELINES
Optimize Traditional Data Pipelines
Process your datasets and build business-critical pipelines consistently and reliably using the cloud data engineering tools and engines of your choice, whether Apache Hadoop, Hive, Spark, Presto, Airflow, or others.
Process Streaming Data
Ingest and process continuously generated data. Execute a variety of time-sensitive applications such as location-based mobile tracking, fraud detection, and real-time customer service interactions with near real-time data.
ORCHESTRATE DATA PIPELINES
Data Pipeline Automation
Have automated repetitive execution of long-standing data preparation and ingestion tasks while allowing users to define custom success or failure criteria.
Leverage Popular Workflow Tools
Schedule multiple commands execution, automate data preparation and ingestion with Qubole Scheduler. Author, schedule, and monitor data pipelines with Qubole Airflow as-a-service.
DELIVER DATA PIPELINES
Extract More Value from Your Data
Review and refine data pipelines with new data and deliver on predefined schedules or on-demand.
Publish in Multiple Ways
Publish data through notebooks, templates, or downstream applications. Use seamless integrations with Github and AWS S3 to run data pipelines.
