DATA LAKE SUMMIT

Ad-hoc Analytics

SQL ON DATA LAKE

Higher productivity, faster time to value and scale to support a larger number of concurrent users.

Obstacles Faced by Data AnalystsSolution
Balance accessibility and performance tradeoffs for growing set of users
Face infrastructure, data management, data ingestion delays to provide valuable insights on specific issuesHave a self service access scalable ad-hoc analytics solution with automated data pipelines
Build data pipelines manually every time to do proof of concept for new types of regular reports

Higher Productivity and Faster Time to Value

  • Get started with easy-to-use SQL interfaces that work the way analysts want
  • Discover insights, query data, analyze results, and debug queries from a single pane of glass Qubole Workbench.
  • Leverage built-in connectors or JDBC and ODBC drivers with popular BI tools like Looker and Tableau to visualize the data
  • Ingest data into the platform with popular tools like Talend, Informatica, Ascend.io

Scale with Data and Queries

  • Maintain price-performance balance for complex queries due to the query or the data or both
  • Improve workload performance by implementing a data layout strategy such as columnar data formats, statistics collections
  • Power ad-hoc or batch queries on large datasets with cloud-optimized open-source frameworks Presto, Hive, SparkSQL

Support 1000s of Concurrent Users

  • Handle burstiness of ad-hoc queries from multiple end-users without operational complexity
  • Minimize costs automatically while supporting concurrent user growth without a performance impact
  • Have near-zero management overhead regardless of inbound query flow
  • Scale up or down automatically to support all workloads at any point in time

Ecosystem Partners

Data Discovery Tools – Qubole Workbench
Tableau Data Sheet
Right Tool for the Job: Using Qubole Presto for Interactive and Ad-Hoc Queries