This webinar demonstrated how Qubole Pipelines Service helps Data Engineers to operationalize complex streaming ETL workloads. We showed how it helps in moving data with transformations and in real-time from sources such as, kafka and Kinesis to targets such as, S3, HIVE, and Snowflake.
Watch this webinar to see a demonstration of how you can operationalize complex streaming and benefit from:
- Lower TCO at high performance: Qubole’s Spark Structured Streaming-as-a-service with advanced auto-scaling capabilities and reliability with exactly one semantics.
- Higher Productivity: A management platform to build, test, deploy, monitor, and manage streaming ETL pipelines.
- Wide Connectivity: Lower Time-to-Insight with wide range of connectivity to various Real-Time, NoSQL, Data Warehouse stores to support end-to-end real-time analytics.
- Open Source standard conformity and Multi-Cloud: To future-proof your solution, associate your workloads to OSS standards which can be seamlessly ported from one public cloud to another.
In addition to this, we will demonstrate how to:
- Drive down the costs of your big data workloads by 50%
- Utilize Qubole for your data engineering, analytics and data science use cases
- Increase efficiency by leveraging cloud technologies that match your workflows
- Extract more value with Machine Learning and AI
- Optimize Apache Spark, Presto, Hadoop/Hive, and Airflow usage
- Use Jupyter and/or Zeppelin Notebooks with Qubole
Explore Qubole and get answers to the big data, analytics, and machine learning questions that are important to you.
