AD HOC ANALYTICS

Challenges of Data Analytics	Data Lake Solution
Balance accessibility and performance tradeoffs for growing set of users
Face infrastructure, data management, data ingestion delays to provide valuable insights on specific issues	Have 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

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

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

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