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  1. Stackups
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  5. Amazon AppFlow vs OpenRefine

Amazon AppFlow vs OpenRefine

OverviewDecisionsComparisonAlternatives

Overview

OpenRefine
OpenRefine
Stacks33
Followers68
Votes0
GitHub Stars11.6K
Forks2.1K
Amazon AppFlow
Amazon AppFlow
Stacks9
Followers42
Votes0

Amazon AppFlow vs OpenRefine: What are the differences?

Introduction

Amazon AppFlow and OpenRefine are two popular tools used in data management and transformation. Each tool has its unique features and capabilities that cater to different use cases. Below are the key differences between Amazon AppFlow and OpenRefine.

  1. Data Integration Capabilities: Amazon AppFlow primarily focuses on data integration by allowing users to securely transfer data between AWS services and SaaS applications. It provides a seamless way to automate data flows without the need for manual intervention. On the other hand, OpenRefine is more oriented towards data cleaning and transformation tasks, such as removing duplicates, correcting inconsistencies, and standardizing data formats, making it a powerful tool for data quality enhancement.

  2. Cloud vs. Local Processing: Amazon AppFlow operates in the cloud, allowing users to leverage the scalability and flexibility of cloud resources for processing their data. This enables users to handle large volumes of data efficiently and effectively. In contrast, OpenRefine is typically used as a desktop application, performing data processing tasks locally on the user's machine. While this provides users with more control over their data, it may limit the scalability of data processing operations.

  3. Automation and Workflow: Amazon AppFlow offers built-in automation capabilities that enable users to create and schedule data flows, monitor data transfers, and set up triggers for data integration tasks. This automation functionality simplifies the process of managing complex data pipelines and ensures data consistency and reliability. OpenRefine, on the other hand, relies on user-driven interactions for data cleaning and transformation tasks, requiring manual input and oversight at each step of the process.

  4. Security and Compliance: Amazon AppFlow provides robust security features, including encryption of data in transit and at rest, access controls, and compliance with industry standards and regulations. This ensures that data transfers are secure, auditable, and compliant with data privacy requirements. In comparison, OpenRefine may pose security risks when handling sensitive or confidential data, as it lacks the advanced security measures and certifications of a cloud-based service like Amazon AppFlow.

  5. Scalability and Performance: Amazon AppFlow is designed to handle large-scale data integration tasks efficiently, leveraging the resources of the AWS cloud infrastructure to ensure high performance and scalability. This makes it suitable for organizations dealing with massive amounts of data and requiring robust data transfer capabilities. OpenRefine, while powerful for smaller-scale data cleaning and transformation tasks, may struggle to scale effectively when dealing with extensive datasets or complex data processing workflows.

In Summary, Amazon AppFlow excels in data integration, automation, and scalability in the cloud, while OpenRefine is more focused on data cleaning, transformation, and local processing, catering to different data management needs and use cases.

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Advice on OpenRefine, Amazon AppFlow

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
Amazon AppFlow
Amazon AppFlow

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 a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. With AppFlow, you can run data flows at nearly any scale at the frequency you choose - on a schedule, in response to a business event, or on demand. You can configure data transformation capabilities like filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps. AppFlow automatically encrypts data in motion, and allows users to restrict data from flowing over the public Internet for SaaS applications that are integrated with AWS PrivateLink, reducing exposure to security threats.

Faceting; Clustering; Editing cells; Reconciling; Extending web services
Point and click user interface; Native SaaS integrations; Enterprise grade data transformations; High scale data transfer; Data privacy defaults through PrivateLink; Custom encryption keys; IAM policy enforcement; Flexible data flow triggers; Easy to use field mapping; Built in reliability
Statistics
GitHub Stars
11.6K
GitHub Stars
-
GitHub Forks
2.1K
GitHub Forks
-
Stacks
33
Stacks
9
Followers
68
Followers
42
Votes
0
Votes
0
Integrations
Python
Python
Dask
Dask
Ludwig
Ludwig
Vertica
Vertica
Google Analytics
Google Analytics
Slack
Slack
Dynatrace
Dynatrace
Datadog
Datadog
Zendesk
Zendesk
Marketo
Marketo
Snowflake
Snowflake
Amplitude
Amplitude
Veeva
Veeva

What are some alternatives to OpenRefine, Amazon AppFlow?

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.

Druid

Druid

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.

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.

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