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  1. Stackups
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  4. Big Data Tools
  5. Azure Synapse vs OpenRefine

Azure Synapse vs OpenRefine

OverviewDecisionsComparisonAlternatives

Overview

OpenRefine
OpenRefine
Stacks33
Followers68
Votes0
GitHub Stars11.6K
Forks2.1K
Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10

Azure Synapse vs OpenRefine: What are the differences?

## Introduction

Azure Synapse and OpenRefine are two popular data management tools used for different purposes in the field of data analytics and processing.

1. **Data Integration Capabilities**: Azure Synapse is primarily designed for large-scale data integration, combining data warehousing, big data analytics, and data integration all in one service. On the other hand, OpenRefine focuses on data cleaning, transformation, and reconciliation, providing a visual interface for users to clean and work with messy data efficiently.

2. **Scalability**: Azure Synapse offers highly scalable solutions that can handle petabytes of data, making it ideal for enterprise-level processing needs. In contrast, OpenRefine is more suited for smaller datasets and may not scale efficiently to handle the requirements of large-scale data processing.

3. **Cloud vs. Local Installation**: Azure Synapse is a cloud-based service provided by Microsoft, allowing users to leverage the scalability and accessibility of the cloud for data management tasks. On the contrary, OpenRefine is a desktop application that needs to be installed locally on the user's machine, which may limit its accessibility and collaboration capabilities.

4. **Automation and Orchestration**: Azure Synapse provides built-in automation and orchestration tools for managing complex data workflows, enabling users to automate tasks and streamline data processes effectively. OpenRefine, while highly efficient for data cleaning tasks, lacks advanced automation and orchestration features, requiring more manual intervention.

5. **Collaboration Features**: Azure Synapse offers collaborative features that enable multiple users to work on the same data pipeline simultaneously, facilitating teamwork and coordination in data projects. In comparison, OpenRefine is more focused on individual data transformation tasks and may not provide robust collaboration tools for team-based projects.

6. **Costs and Licensing**: Azure Synapse is a paid cloud service with pricing based on usage and data storage, offering different tiers for various user needs. OpenRefine, on the other hand, is an open-source tool that is free to use, making it a cost-effective option for users with limited budgets.

In Summary, Azure Synapse is focused on large-scale data integration and processing in the cloud, while OpenRefine excels in data cleaning and transformation tasks on smaller datasets with a local installation.

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Advice on OpenRefine, Azure Synapse

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

OpenRefine
OpenRefine
Azure Synapse
Azure Synapse

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.

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.

Faceting; Clustering; Editing cells; Reconciling; Extending web services
Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
GitHub Stars
11.6K
GitHub Stars
-
GitHub Forks
2.1K
GitHub Forks
-
Stacks
33
Stacks
104
Followers
68
Followers
230
Votes
0
Votes
10
Pros & Cons
No community feedback yet
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Concurrency
  • 1
    Dictionary Size Limitation - CCI
Integrations
Python
Python
Dask
Dask
Ludwig
Ludwig
Vertica
Vertica
No integrations available

What are some alternatives to OpenRefine, Azure Synapse?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

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.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

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