Druid vs Snowflake

Overview

Druid

Stacks376

Followers867

Votes32

Snowflake

Stacks1.2K

Followers1.2K

Votes27

Druid vs Snowflake: What are the differences?

Introduction

This markdown code provides a comparison between Druid and Snowflake in terms of their key differences.

Scalability: Druid is designed to handle real-time querying and analysis of large datasets with sub-second query latencies. It uses distributed architecture and can handle petabytes of data. In contrast, Snowflake is a cloud data warehouse that can handle massive amounts of structured and semi-structured data. It scales well for large datasets and concurrent users, but its query latency may be higher compared to Druid.
Data Types and Storage: Druid is optimized for time-series data and has native support for time-based aggregations and filtering. It stores data in a columnar format, which enables efficient compression and fast querying. On the other hand, Snowflake supports a wide range of data types and stores data in a structured columnar format, allowing for efficient storage and retrieval of different data types.
Querying Abilities: Druid supports interactive queries on real-time and historical data and provides fast aggregations and filtering capabilities. It also supports complex event processing and streaming data ingestion. Snowflake, on the other hand, supports both real-time and batch processing. It offers advanced SQL querying capabilities and supports complex joins and aggregations on large datasets.
Data Ingestion and Updates: Druid supports real-time data ingestion using its native ingestion framework, which enables continuous data ingestion and indexing for real-time querying. It also supports batch data ingestion for historical data. Snowflake, on the other hand, supports both real-time and batch data ingestion. It has built-in connectors for various data sources and supports data updates and deletes.
Query Performance and Optimization: Druid is optimized for fast query performance and provides features like pre-aggregation and indexing to improve query speed. It uses caching and query optimization techniques to minimize query latencies. Snowflake, on the other hand, uses a combination of columnar storage, query optimization, and workload management to provide optimal performance. It automatically scales resources based on the workload and provides automatic query optimization.
Cost and Pricing Model: Druid is an open-source project, so there are no upfront costs associated with its usage. However, it requires infrastructure setup and maintenance costs. Snowflake is a cloud-based service and follows a pay-as-you-go pricing model. It offers different pricing tiers based on usage and provides flexibility in terms of scaling resources up or down, depending on the needs.

In summary, Druid and Snowflake differ in scalability, data types and storage, querying abilities, data ingestion and updates, query performance and optimization, and cost and pricing model.

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CLI (Node.js)

Manual

Detailed Comparison

Druid	Snowflake
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.	Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
Statistics
Stacks 376	Stacks 1.2K
Followers 867	Followers 1.2K
Votes 32	Votes 27
Pros & Cons
Pros 15 Real Time Aggregations 6 Batch and Real-Time Ingestion 5 OLAP 3 OLAP + OLTP 2 Combining stream and historical analytics Cons 3 Limited sql support 2 Joins are not supported well 1 Complexity	Pros 7 Public and Private Data Sharing 4 Multicloud 4 User Friendly 4 Good Performance 3 Great Documentation
Integrations
Zookeeper	Python Apache Spark Node.js Looker Periscope Mode

What are some alternatives to Druid, Snowflake?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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Druid vs Snowflake: What are the differences?

Introduction

This markdown code provides a comparison between Druid and Snowflake in terms of their key differences.

Scalability: Druid is designed to handle real-time querying and analysis of large datasets with sub-second query latencies. It uses distributed architecture and can handle petabytes of data. In contrast, Snowflake is a cloud data warehouse that can handle massive amounts of structured and semi-structured data. It scales well for large datasets and concurrent users, but its query latency may be higher compared to Druid.
Data Types and Storage: Druid is optimized for time-series data and has native support for time-based aggregations and filtering. It stores data in a columnar format, which enables efficient compression and fast querying. On the other hand, Snowflake supports a wide range of data types and stores data in a structured columnar format, allowing for efficient storage and retrieval of different data types.
Querying Abilities: Druid supports interactive queries on real-time and historical data and provides fast aggregations and filtering capabilities. It also supports complex event processing and streaming data ingestion. Snowflake, on the other hand, supports both real-time and batch processing. It offers advanced SQL querying capabilities and supports complex joins and aggregations on large datasets.
Data Ingestion and Updates: Druid supports real-time data ingestion using its native ingestion framework, which enables continuous data ingestion and indexing for real-time querying. It also supports batch data ingestion for historical data. Snowflake, on the other hand, supports both real-time and batch data ingestion. It has built-in connectors for various data sources and supports data updates and deletes.
Query Performance and Optimization: Druid is optimized for fast query performance and provides features like pre-aggregation and indexing to improve query speed. It uses caching and query optimization techniques to minimize query latencies. Snowflake, on the other hand, uses a combination of columnar storage, query optimization, and workload management to provide optimal performance. It automatically scales resources based on the workload and provides automatic query optimization.
Cost and Pricing Model: Druid is an open-source project, so there are no upfront costs associated with its usage. However, it requires infrastructure setup and maintenance costs. Snowflake is a cloud-based service and follows a pay-as-you-go pricing model. It offers different pricing tiers based on usage and provides flexibility in terms of scaling resources up or down, depending on the needs.

In summary, Druid and Snowflake differ in scalability, data types and storage, querying abilities, data ingestion and updates, query performance and optimization, and cost and pricing model.