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  1. Stackups
  2. Application & Data
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  4. Big Data Tools
  5. Azure HDInsight vs Druid

Azure HDInsight vs Druid

OverviewComparisonAlternatives

Overview

Druid
Druid
Stacks376
Followers867
Votes32
Azure HDInsight
Azure HDInsight
Stacks29
Followers138
Votes0

Azure HDInsight vs Druid: What are the differences?

1. Data Processing Paradigm: Azure HDInsight is a fully managed cloud service that supports various big data processing frameworks like Hadoop, Spark, and Hive, which use a batch-oriented data processing model. In contrast, Druid is an open-source data store designed for real-time analytic queries on large datasets using a column-oriented storage format, making it more suitable for real-time analytics and interactive queries.

2. Data Ingestion: Azure HDInsight allows users to ingest data from various sources into the Hadoop ecosystem through tools like Apache Flume, Sqoop, and Storm. Druid, on the other hand, provides native support for real-time data ingestion using its native ingestion system, making it easier to ingest streaming data directly into the system without additional integration.

3. Query Performance: Azure HDInsight relies on MapReduce and Spark for processing data, which may lead to longer query response times for complex analytical queries. Conversely, Druid's architecture optimized for fast querying allows users to run sub-second queries on massive datasets, making it more suitable for interactive analytics applications.

4. Scalability and Elasticity: Azure HDInsight offers scalability through the ability to add or remove compute nodes dynamically based on workload requirements, ensuring resource utilization optimization. Druid, on the other hand, is designed for horizontal scalability and can handle large datasets by adding more nodes to the cluster easily.

5. Query Language and APIs: Azure HDInsight supports various query languages like HiveQL, Pig Latin, and Spark SQL for data processing and analytics. In contrast, Druid supports a SQL-like query language called Druid SQL and provides various APIs for interacting with the system, making it easier for developers to query data using familiar interfaces.

6. Use Cases: Azure HDInsight is suitable for batch-oriented processing, ETL workflows, and traditional data warehousing applications. Druid, on the other hand, is more focused on real-time analytics, event-driven applications, and use cases requiring low-latency queries on large datasets.

In Summary, Azure HDInsight and Druid differ in terms of data processing paradigms, data ingestion methods, query performance, scalability, query language support, and use cases they cater to.

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Detailed Comparison

Druid
Druid
Azure HDInsight
Azure HDInsight

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.

It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.

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Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises
Statistics
Stacks
376
Stacks
29
Followers
867
Followers
138
Votes
32
Votes
0
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
No community feedback yet
Integrations
Zookeeper
Zookeeper
IntelliJ IDEA
IntelliJ IDEA
Apache Spark
Apache Spark
Kafka
Kafka
Visual Studio Code
Visual Studio Code
Hadoop
Hadoop
Apache Storm
Apache Storm
HBase
HBase
Apache Hive
Apache Hive
Azure Data Factory
Azure Data Factory
Azure Active Directory
Azure Active Directory

What are some alternatives to Druid, Azure HDInsight?

Google BigQuery

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

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

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

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

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

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

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

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

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

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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