What are the best ETL cloud migration tools for data warehousing?

Talend Studio and combination of Cloud Integration gives you best of both worlds. Talend Studio gives you the depth to build transformations (if needed). You can build dynamic extracts and generate both DDL and DML in the target schema (agnostic) i.e. any database in any cloud. Other strengths of Talend is the Data Quality studio , Data Preparation, Data Pipelies and Data Stewardship on cloud for additional activities of your data migration journey, which are logical and common hurdlles. Can also extend to use Data Catalog. But the good news is Talend can be the one stop solution in this entire end to end journey. Talend is also quite strong integrating with BIG Data and has all other in-built connectors for Git, Bitbucket etc.,

Azure Data Factory is best among the listed options in my opinion with the following features i- as it has many pre-configured options referred to as Linked Services. ii- It's user-friendly and datasets can even be managed in a folder hierarchy. iii- The git configuration allows you to code version control iv- Data flows have advanced ETL options v- ADF is almost covering all data formats that ETL developers are required to handle in day to day tasks

All depends on what you need the ETL tool for and what your data model looks like. All of them have got pros and cons. Personally, I would like something which uses the metadata to drive ETL process. Of Course it is not possible to implement complex logic with just metadata. However, making the solution metadata driven as much as possible would be my choice. I personally prefer to build simple application which is driven by metadata and dynamically creates the pipeline rather than using an ETL tool. Gives me flexibility to design the app as per business need and ability to optimise it for scalability and performance etc. There is limit of 750 chars so can't elaborate but we have built solutions which fully optimised and easily maintained.

If AWS lock in is not a concern, AWS Glue stands out with its extensive support for various languages like Pyspark, Python and an extensive metadata support. We were able to convert a large amount of on premesis ETL using Glue and were able to run a enterprise wide support of datasets usage discovery to support CCPA.

AWS Glue stands out as a leading ETL cloud migration tool for data warehousing, thanks to its tight integration with the AWS ecosystem and a fully managed, serverless architecture powered by Apache Spark. Offering scalability and high-performance ETL processing, it effortlessly handles diverse data sources and formats. The centralized Data Catalog streamlines governance and discovery processes, while seamless integration with Amazon S3, Redshift, Athena, and EMR enhances its versatility. With these features, AWS Glue emerges as a compelling choice for organizations seeking a clean and efficient migration solution for their data warehousing needs in the cloud. Developers can write Glue Jobs with Python, Scala, and Pyspark.

Data factory is very easy to use with its simple drag and drop "no code" interface, has a range of connectors for whatever source and sink you're connecting to, and can do more advanced transformations with the Data Flow component (although you need to calculate what the compute costs would be to make sure it works). I generally recommend most organisations start with ADF to see if it's a fit before doing something that requires code for ETL.

ADF is a good tool to leverage in an Orchestration/Ingestion Framework. It is a great complementary tool with Databricks which is more suitable for complex ETL jobs.

In my experience Azure data factory is one of the best Etl cloud based tool, For example, suppose you want to use ADF to copy data from a CSV file in Azure Blob Storage to a table in Azure SQL Database, and then perform some transformations on the data, such as filtering, sorting, and grouping. You can create a pipeline with the following activities.

Catch all generic tool that lets you build pipelines but not best of breed and requires many different steps to be configured manually. It will get the job done but it wont be the easiest and quickest tool to build pipelines. There are much better alternatives like Matillion which is easier to use, has more features and can speed up building pipelines by a wide margin compared to ADF. work.

Azure Data Factory is a solid ingestion tool. It does the E for Extraction and L for Loading parts of ETL extremely well. It has a wide variety of connectors and its conditional flow, looping constructs, calling child pipeline capabilities and well thought out triggering capabilities make ADF a pretty solid and dependable cloud migration tool. It can allow some nifty tricks and manipulation through its expression language. Good thing is - it keeps on adding new features e.g. Airflow integration. Its integration with Azure Devops is very attractive too. All in all, this is a solid, performant tool which is almost a no-brainer to use if you have an Azure stack.

Cloud Data Fusion is a fully managed, cloud-native, enterprise data integration service for quickly building and managing data pipelines. Cloud Data Fusion web interface lets you build scalable data integration solutions to clean, prepare, blend, transfer, and transform data, without having to manage the infrastructure. It is powered by the open source project CDAP. 1. Visual point-and-click interface enabling code-free deployment of ETL/ELT data pipelines 2. Natively integrated best-in-class Google Cloud services Cloud Data Fusion instance contains a unique, independent Cloud Data Fusion deployment that contains a set of services, which handle pipeline lifecycle management, orchestration, coordination, and metadata management

Google Cloud Data Fusion is a cloud-native ETL service with a simple drag-and-drop interface, ideal for users in the Google Cloud ecosystem. It supports various Google Cloud services, making data integration seamless. It's efficient for data transformation, validation, and profiling, but its ease of use may come at the cost of advanced customization for complex tasks. While excellent for Google Cloud-centric workflows, its utility might be limited in multi-cloud or non-Google environments. Data Fusion is a top choice for those prioritizing ease of use and integration within Google Cloud, but less so for complex, multi-cloud data operations.

Google Cloud Data Fusion is a fully managed, cloud-native ETL (Extract, Transform, Load) service provided by Google Cloud Platform (GCP). It is designed to simplify the ETL process and enable organizations to efficiently move and transform data from various sources into their data warehouses. - Google Cloud Data Fusion is built on an open-source foundation (CDAP - Cask Data Application Platform) and is designed to run natively on Google Cloud Platform. - The platform provides a wide range of pre-built connectors to various data sources, including Google Cloud Storage, BigQuery, Cloud SQL, and external sources like Amazon S3, Azure Storage, and more. - The platform also provides features for data quality monitoring and lineage tracking.

I recommend adopting Google Cloud Platform (GCP) for your ETL and data warehousing needs. GCP offers versatile and user-friendly tools such as Google Cloud Dataflow, BigQuery, and Cloud Composer, providing a robust solution with excellent performance. This choice aligns well for versatility, ease of learning, and overall efficiency.

GCP Cloud Datafusion is good etl tool while you are in the GCP landscape. it connects quickly to many gcp PaaS System and do the job seamlessly. typcially , While you use g4 google analytics API, Data is very high, this data fusion is good to do ETL for high level nested json data set

Talend Cloud Talend Cloud Unify all your data: On-prem, cloud, hybrid? No problem. Integrate & manage it all. Clean & reliable data: Ensure accuracy & boost application performance with data quality tools. Easy, no-code pipelines: Build complex data flows without writing tons of code. Scalable & secure: Grows with your needs & keeps your data safe. Share & collaborate: Securely share data across teams & improve decision-making.

Talend Cloud stands as the ETL tool of choice, surpassing others in both cost-effectiveness and performance. Its subscription-based pricing model, factoring in users, connectors, and features, ensures a tailored and transparent cost structure, distinguishing it from competitors like cloud data factory with their pay-as-you-go model. Supporting major cloud data warehouses (AWS, Azure, Snowflake, Google Cloud) for both batch and real-time processing, Talend Cloud excels, making it the clear frontrunner for organizations seeking a balance of affordability and top-tier data integration performance.

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Talend Cloud, an ETL tool, excels in integrating diverse data sources, from cloud services to databases. Its user-friendly interface suits both technical and non-technical users, aiding in complex data tasks. However, its wealth of features can be daunting for beginners, and complex operations may impact performance. Ideal for businesses needing versatile data integration, Talend Cloud may be overly complex for smaller or simpler data requirements.

5. Talend Cloud ???: ? Talend Cloud, a unified storm of integration, covers both ETL and data integration. With extensive connectors and a comprehensive approach, it stands tall for those seeking a unified solution in complex data integration scenarios. ? Key Features: Unified integration platform, extensive connectors. ? Pros: Support for hybrid cloud, comprehensive solution. ? Cons: May require substantial resources. ? Best Scenario: Perfect for those requiring a unified solution, especially in complex data integration and end-to-end data pipeline orchestration.

I will consider Snowflake. It has more flexibility to connect with three different cloud environments like AWS , Azure, Google. Also it can be easily configured with coding languages as well as different visualisation tools.

Other notable ETL tools for data warehousing include: Informatica Cloud Data Integration: Known for robust data governance and quality, supports complex transformations, high performance. Fivetran: Automates data integration with wide source connectivity, praised for simplicity and minimal configuration needs. Matillion: Tailored for cloud data warehouses like Snowflake and Redshift, offers batch and real-time processing with a user-friendly interface. Apache NiFi: Open-source, highly configurable for complex data flows, excels in fine-grained processing. Hevo Data: No-code platform, excels in real-time integration, simple and reliable with pre-built source integrations.

As a data and analytics firm deeply involved in cloud migrations and modernisation, I've observed firsthand the transformative impact of cloud migration tools in data warehousing. Tools like AWS Glue, Azure Data Factory, and Informatica Cloud are pivotal in this landscape. We have done many critical projects when it comes to Informatica particularly. Their on-prem to cloud tool is definitely helpful. The success rate of automation is high but, it's important to approach these migrations with thorough assessments, as complexities are often part of the journey. Many entities underestimate the importance of having a skilled team of ETL engineers who can navigate these challenges effectively.

I would rather go for Talend Cloud as it offers a user-friendly and intuitive interface that suits both technical and non-technical. It provides flexibility in connectivity and wide range of connectors which is crucial for organisations. Its also versatile allowing for handling different scenarios in data integration and real-time processes. Monitoring of performance of jobs is another key point.

Data bricks Is one of the better solution In terms of price and performance for SQL and BI workloads moving to cloud data warehouse from legacy. Some of the good features are Simplified server less management, AI optimised query execution, Simple workflow for the batch and streaming and End to end pipeline monitoring