Data Engineers/Architects

Spark for Batch Workloads

Data Scientists are transitioning to distributed engines to develop Machine Learning models and achieve a data-driven approach.

However, when moving to enterprise data science on a distributed Spark architecture, data scientists find it challenging to manage package dependencies for multiple dialects like Python & R, which increases time to market and can lead to abandoning the distributed framework.

Spark offers a frictionless experience with easy access to cluster computing resources through multiple interfaces and the flexibility to choose Scala, Java, Python, or R.

Easily Control Costs

Data Engineering for Data Pipelines

Qubole supports multiple engines and notebooks, providing a choice for data engineering teams to build pipelines using SQL, Python, Scala, Spark, Hive, and streaming data services.

Users can create, schedule and manage workloads with Continuous Data Engineering services, using Zeppelin or Jupyter notebooks. Qubole offers integrations with Talend, Big Query, AWS Data Lake Formation, and APIs/SDKs to integrate other processes with data.

Avoid bottlenecks in data preparation and ingestion by using Qubole to easily explore, build and deliver data pipelines, and meet your big data engineering requirements.

Ease of Use

To gain a competitive edge from data, businesses need more than just a good BI tool. From interactive analytics to deep learning, Qubole’s cloud data lake platform offers an optimal architecture for complex requirements.

With a simple, open, and secure platform, Qubole addresses diverse use cases, data formats, and clouds, providing flexibility in a rapidly growing environment.

This empowers engineers to focus on data processing, minimizing DevOps support for cluster operations. Qubole’s user interface also offers a great Sandbox for Hive queries and Spark jobs, making it easy to implement and use.

Workflow Management

Want to create dynamic, extensible, and scalable data pipelines, while leveraging Qubole’s fully managed and automated cluster lifecycle management? With Airflow, you can programmatically author, schedule, and monitor workflows, visualizing dependencies, progress, logs, code, tasks, and success status.

It provides insights into execution time and task completion patterns. Notably, Airflow excels in representing directed acyclic graphs (DAGs) of tasks using Python scripts, enabling data engineers to easily handle complex workflows and perform transformations on intermediate data sets using Python utilities.