TrustRadius

Apache Druid vs. BlazingSQL

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Druid
Score 10.0 out of 10
N/A
Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture.N/A
BlazingSQL
Score 0.0 out of 10
N/A
BlazingSQL (formerly BlazingDB) is an Open-Source solution for SQL in Python. BlazingSQL provides a high-performance distributed SQL engine in Python. Built on the RAPIDS GPU data science ecosystem, ETL massive datasets on GPUs. BlazingSQL lets users ETL raw data directly into GPU memory as a GPU DataFrame (GDF). RAPIDS.ai is a suite of open-source libraries that allow the user to run end to end data science and analytics pipelines on GPUs.N/A
Pricing
Apache DruidBlazingSQL
Editions & Modules
No answers on this topic
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Offerings
Pricing Offerings
DruidBlazingSQL
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Best Alternatives
Apache DruidBlazingSQL
Small Businesses
InfluxDB
InfluxDB
Score 9.0 out of 10
InfluxDB
InfluxDB
Score 9.0 out of 10
Medium-sized Companies

No answers on this topic

SQLite
SQLite
Score 8.0 out of 10
Enterprises

No answers on this topic

SQLite
SQLite
Score 8.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache DruidBlazingSQL
Likelihood to Recommend
9.0
(0 ratings)
-
(0 ratings)
User Testimonials
Apache DruidBlazingSQL
Likelihood to Recommend
It is extremely well suited to rapid ingest of data from large data sources, due to the fact that you can restrict what is ingested by column/field, so that you only pull in the data you actually want or need.
As stated earlier, the open source version could use better cluster management tools, and troubleshooting tools for failing jobs/tasks.
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Pros
  • Rapid ingest
  • Limiting ingest to only the relevant fields/columns
  • Easy ingest spec creation
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Cons
  • Security configuration is problematic
  • Cluster management could have more features
  • Troubleshooting incomplete tasks/jobs is a chore
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Anonymous
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Return on Investment
  • Integration with S3 storage has saved about 35% on our storage, over HDFS
  • The rapid ingest has saved user's time in the query aspects of their applications.
  • The ability to ingest from a variety of data sources has made overall user application queries much simpler
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Anonymous
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ScreenShots