Firebolt - Apache Druid Alternative

Firebolt Fast Analytics vs. Druid

Architecture

Druid is a customer managed processing engine designed primarily for OLAP use cases. Druid requires multiple role specific servers, external metadata store and deep storage, making it complex and resource intensive to size, deploy and manage.

Firebolt is a columnar Data Warehouse architected from the ground up for low-latency analytics workloads at TB++ scale with built-in storage optimization. As a SaaS offering, there are no instances or role specific servers to manage.

Decoupled Compute and Storage

Druid does not leverage decoupled compute and storage. While it uses deep storage for persistence, it does not use this data for queries. Due to the reliance on memory and internal storage, Druid deployments may need larger number of nodes.

Decoupled compute and storage is a key design element in Firebolt, allowing independent scaling of compute and unlimited storage. Provides control and visibility of resources for easy, cost effective scaling and workload isolation.

Operational Overhead

Installation, management, upgrades and scaling of a Druid cluster require extensive amount of resources. Requires careful planning and execution around server and storage sizing.

As a SaaS offering, Firebolt abstracts away the complexity of managing infrastructure. Scaling with cloud based resources is performed through Firebolt UI, SQL or APIs.

SQL and Ecosystem Integration

Druid supports a native query language and Druid SQL (limited SQL). Not all features available in the native query language are supported with Druid SQL. Druid offers limited third party ecosystem integration.

Firebolt leverages ANSI-SQL with built-in Query Optimizer and SQL IDE for SQL queries and scaling. Orchestrate through APIs and SDKs or through a growing set of modern integrations including the likes of dbt, Airflow, Superset, Cube and others.

Data Apps

While Druid provides low latency and high concurrency, the cost of delivering this requires sizable infrastructure investments combined with significant skilled resources to operate. While managed versions of Druid promise de-coupled compute and storage capabilities, they do not offer workload isolation or ease of environment management through SQL.

Firebolt provides Low latency at TB++ scale and High concurrency at lower TCO due to efficient use of infrastructure components. Furthermore, with de-coupled compute and storage, Firebolt enables workload isolation and easy spin-up/spin-down of environments through SQL.

Sub-second Analytics and Concurrency

As customers build data warehouse based data apps, the need for sub-second performance and high number of queries per second are challenging. While Druid delivers fast performance, this performance comes at a cost. The cost is in the number of components that need to be sized, configured, maintained and not to mention the skills required for continuous upkeep.

Firebolt consumes less resources, delivers sub-second analytics and high concurrency at the same time. Firebolt leverages various types of indexes to deliver fast, high concurrency analytics. Additionally, customers have the option of adding multiple engines, each with scale-out capabilities, to increase concurrency, resource and workload isolation.

Data Model

Druid was not designed to natively support Joins and denormalized data model is considered a best practice. Similarly, nested JSON needs to be flattened prior to loading into Druid.

Firebolt does not require a denormalized data model and executes joins in sub-seconds. Additionally, broad set of choices, including Lambda expressions, are available when working with JSON data.

Ingestion

Druid allows ingest of batch and streaming data, however, complex JSON based ingestion specs are required.

Firebolt continuous ingestion process is simplified through SQL. As simple as “Insert into … Select * from …”. No need for complex JSON specs.

Physical data layout

Data partitioning is based on timestamp only. While secondary partition can be specified, if queries are based on the secondary index, it will require the scanning of all time series data.

Firebolt uses sparse indexes for highly efficient data pruning. Partitions are flexible and optional.

Aggregations and Roll-ups

Druid roll-ups summarize data based on an ingestion spec. To address lack of granularity in roll-ups, multiple independent roll-ups will need to be defined and maintained.

Firebolt’s aggregating indexes are defined once, incrementally auto-synced at ingest and data can be queried at index speed immediately. All with online access to raw data.

Pricing Model

Druid complexity, in terms of infrastructure sizing and lack of decoupled compute and storage, results in unpredictable costs especially as your data volume grows.

Firebolt lowers TCO through granular choice of instances without the complexity of cluster configuration, scale to zero with auto-stop and optimized object storage format.

APACHE DRUID ALTERNATIVE

Get the speed you need, without the complexity

Architecture

Decoupled Compute & Storage

Operational Overhead

SQL & Ecosystem Integration

Data Apps

Sub-second Analytics & Concurrency

Data Model

Ingestion

Physical data layout

Aggregations & Roll-ups

Pricing Model

Firebolt Fast Analytics vs. Druid

Architecture

Decoupled Compute and Storage

Operational Overhead

SQL and Ecosystem Integration

Data Apps

Sub-second Analytics and Concurrency

Data Model

Ingestion

Physical data layout

Aggregations and Roll-ups

Pricing Model

Quick Benchmark Snapshot

The Firebolt Advantage

Cloud-native Architecture

Integration with Modern Data Stack

Performance

Cost Efficiency

Some happy clients

Firebolt has outperformed all other data serving SQL engines we have tested. It is built by engineers - for engineers

Roy MiaraEngineering ManagerExplorium

Firebolt immediately gave us faster performance at a much greater scale, which let our customers analyze huge datasets with sub-second performance.It also gave us the flexibility to deliver complex data features at a much faster pace

Yoav ShmariaVP R&D, Platform.SimilarWeb

Firebolt removed our analytics limitations. We can now analyze any level of detail, up to hundreds of billions of rows. We see a 183x performance boost - our dashboards now load in seconds, even milliseconds.

Alexandra SudilovskiS. BI Expert & Looker Guild M.AppsFlyer

Join the workshop:

Get started with Firebolt

The Hands-on workshop will cover:

© 2024 Firebolt Analytics Inc. All rights reserved

Roy Miara
Engineering Manager
Explorium

Firebolt immediately gave us faster performance at a much greater scale, which let our customers analyze huge datasets with sub-second performance.
It also gave us the flexibility to deliver complex data features at a much faster pace

Yoav Shmaria
VP R&D, Platform.
SimilarWeb

Alexandra Sudilovski
S. BI Expert & Looker Guild M.
AppsFlyer