Druid vs Dremio | Compare Data Warehousing Tools

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Data warehousing software products like Dremio and Druid enable users to access and analyze their big data to gain actionable insights. So which data tool is better for your data processing needs? This article will compare these data warehousing  tools’ features and capabilities so you can choose the best option for your organization.

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What is Dremio?

Dremio is a data lakehouse platform for organizations to manage their data from various sources. With extensive integrations and intuitive tools, Dremio provides users with complete control over their data workflows and insight processes.

What is Druid?

Druid is an open-source distributed data store that supports data workflows, visibility and ad-hoc analytics. Users of the Druid platform can build data analytic applications or integrate with existing data pipelines to gain valuable information from their datasets.

Head-to-head comparison: Dremio vs. Druid

Data preparation and storage methods

Dremio provides self-service data curation and sharing while enabling users to prepare their data for use without making copies of it. This data warehousing system integrates with AWS Glue, which allows the tool to access data sets so further data preparation is unnecessary. Dremio combines datasets from separate storages and supports SQL querying to process them.

Dremio utilizes Data Reflections for source data, which are maintained in a columnar representation based on Apache Parquet and Apache Arrow. It uses compression methods including delta encoding, dictionary encoding and run-length encoding. Dremio supports snappy compressors for spill operations; these capabilities help save space for disk operations.

Druid has data preparation functions for simple ingestion and utilization within the platform. Its connection with the third-party UI Metatron provides solutions for easy data preparation so that users can analyze and visualize their data quickly. Users may implement Apache Spark technology to support the data preparation process, as performing Spark calculations prepares the data for ingestion in the Druid system.

In addition, Druid utilizes compaction strategies to save data storage space and optimize the segment size for the database. This can increase performance, as optimized segments need less per-segment processing and memory overhead for ingestion and path querying. Other Druid strategies for saving disk storage space include rolling up data at ingestion and utilizing segment partitioning.

Data engineering and SQL functions

Dremio’s fully-managed lakehouse platform facilitates the data engineering process by simplifying data pipeline management, preventing data sprawl and inconsistent reporting, and providing built-in governance and lineage.

Dremio’s transparent query acceleration and SQL DML on the lakehouse results in faster and more expansive data processing capabilities. The platform enables a wide array of SQL functions, including Aggregate, Binary, Bitwise, Boolean, Conditional, Context, Conversion, Data Generation, Datatype, Date/Time, Math, Percentile, String and Window.

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Druid is primarily utilized for business intelligence queries on historical and real-time data. The data can be queried through JSON over HTTP and SQL, and Druid SQL can translate SQL into native Druid queries.

Druid SQL is the built-in SQL layer, enabling more SQL queries to be performed by the solution; the software then executes queries based on their data source type. Druid can support many SQL functions and types, including aggregation functions, multi-value string functions, scalar functions, metadata queries, scans, searches, limits, orders, grouping, offsets, Identifiers and literals, context parameters, time boundaries and Dynamic parameters.

Integration and deployment

Dremio allows users to build interactive dashboards through native connectors. It works with many data sources and BI tools, such as relational databases, cloud sources, local filesystems, Hadoop, AWS, Microsoft, IBM and StreamSets. Additionally, the connection options enable users to analyze data from external sources.

Users can use Dremio’s API inside of their automated data workflows. The platform supports social identity provider integration as well as SOC 2 Type II and GDPR compliance, providing safety throughout the data processes.

The Druid open-source platform has integrations with various other business intelligence solutions, allowing for data streams to occur on large data sets from data lakes, message buses and other data sources. Organizations can use the solution with other data processing tools such as time-series databases, search systems and data warehouses.

Examples of other complementary software tools that can integrate with Druid include Apache Kafka, HDFS, AWS S3 and AWS Kinesis. The Druid software can be deployed on-premises and in the cloud on any Nix environment on commodity hardware.

Choosing the right data warehousing software

Druid can be an excellent choice for users who wish to easily translate SQL into native queries for faster insights. Dremio may be a better option for an organization that desires less data preparation processing. By considering the features of each data warehousing tool, buyers can choose the best options for their data management requirements.