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  5. Druid vs OpenRefine

Druid vs OpenRefine

OverviewDecisionsComparisonAlternatives

Overview

Druid
Druid
Stacks376
Followers867
Votes32
OpenRefine
OpenRefine
Stacks33
Followers68
Votes0
GitHub Stars11.6K
Forks2.1K

Druid vs OpenRefine: What are the differences?

  1. Integration with Big Data Ecosystem: Druid is designed for real-time analytics and is often used as part of a big data processing pipeline, integrating seamlessly with tools like Apache Hadoop, Apache Spark, and Kafka. On the other hand, OpenRefine is primarily a data cleaning and transformation tool that focuses on improving data quality and structure without direct integration with big data ecosystems.

  2. Data Processing Approach: Druid is optimized for fast query performance on large datasets, utilizing a column-oriented data store and distributed architecture to achieve real-time analytics capabilities. OpenRefine, on the other hand, is focused on interactive data exploration, allowing users to visually manipulate and clean data in a user-friendly manner without the need for complex programming or computational resources.

  3. Use Cases and Industries: Druid is commonly used in industries that require real-time analytics such as e-commerce, advertising, and IoT, where immediate insights are crucial for decision-making. OpenRefine, on the other hand, is widely used in data cleaning, preprocessing, and data wrangling tasks across various domains including research, journalism, and data journalism.

  4. Scalability and Performance: Druid is known for its scalability and high performance, able to handle large volumes of data and deliver sub-second query responses for analytical workloads. Although OpenRefine is robust for small to medium-sized datasets, it may not perform as efficiently as Druid when dealing with massive amounts of data or requiring real-time processing.

  5. User Interface and Ease of Use: OpenRefine has a user-friendly interface that allows non-technical users to clean and transform data through a visual and interactive approach, making it ideal for users without extensive programming skills. In contrast, Druid typically requires a more technical skill set for configuration, deployment, and optimization, catering to users with a background in data engineering or analytics.

  6. Community Support and Development: Both Druid and OpenRefine have active communities that contribute to their development and provide support for users. However, Druid's community is more focused on real-time analytics and data applications, while OpenRefine's community is geared towards data cleaning and transformation tasks, reflecting the distinct priorities of each tool's user base.

In Summary, Druid and OpenRefine differ in their integration with big data ecosystems, data processing approach, use cases, scalability, user interface, and community support, catering to distinct requirements in analytics and data management.

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Advice on Druid, OpenRefine

Sarah
Sarah

Jun 25, 2020

Needs adviceonOpenRefineOpenRefine

I'm looking for an open-source/free/cheap tool to clean messy data coming from various travel APIs. We use many different APIs and save the info in our DB. However, many duplicates cannot be easily recognized as such.

We would either write an algorithm or use smart technology/tools with ML to help with product management.

While there are many things to be considered, this is one feature that it should have:

"To avoid confusion, we need to merge the suppliers & products accordingly. Products and suppliers must be able to be merged and assigned separately.

Reason: It may happen that one supplier offers different products. E.g., 1 tour operator offers 3 products via 1 API, but only 1 product with 3 (or a different amount of) variations via a different API. Also, the commission may differ for products, which we need to consider. Very often, products that are live (are bookable in real-time) on via 1 API, but are not live on the other. E.g., Supplier product 1 & 2 of API1 are live, product 3 not. For the same supplier, API2 provides live availability for products 1, 2, and 3.

Summing up, when merging the suppliers (tour operators) we need to consider:

  • Are the products the same for all APIs?
  • Which booking system API gives a better commission? Note: Some APIs charge us 1-5% depending on the monthly sale, which needs to be considered
  • Which booking system provides live availability
  • Is it the same supplier, or is the name only similar?

Most of the time, the supplier names differ even if they are the same (e.g., API1 often names them XX Pty Ltd, while API2 leaves "Pty Ltd" out). Additionally, the product title, description, etc. differ.

We need to write logic and create an algorithm to find the duplicates & to merge, assign, or (de)activate the respective supplier or product. My previous developer started a module to merge the suppliers, which does not seem to work correctly. Also, it is way too time taking considering the high amount of products that we have.

I would recommend merging, assigning etc. products and suppliers only if our algorithm says it's 90- 100% the matching supplier/product. Otherwise, admins need to be able to check & modify this. E.g. everything with a lower possibility of matching will be matched automatically, but can be undone or modified.

The next time the cron job runs, this needs to be considered to avoid recreating duplicates & creating a mess."

I am not sure in what way OpenRefine can help to achieve this and what ML tool can be connected to learn from the decisions the product management team makes. Maybe you have an idea of how other travel portals deal with messy data, duplicates, etc.?

I'm looking for the cheapest solution for a start-up, but it should do the work properly.

19.2k views19.2k
Comments

Detailed Comparison

Druid
Druid
OpenRefine
OpenRefine

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 powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.

-
Faceting; Clustering; Editing cells; Reconciling; Extending web services
Statistics
GitHub Stars
-
GitHub Stars
11.6K
GitHub Forks
-
GitHub Forks
2.1K
Stacks
376
Stacks
33
Followers
867
Followers
68
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
Python
Python
Dask
Dask
Ludwig
Ludwig
Vertica
Vertica

What are some alternatives to Druid, OpenRefine?

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

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.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

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