What ETL tools can you use to integrate data in a warehouse?

The main purpose of ETL/ELT is to extract the data from different sources , transform/load or load/transform as per business requirement and build the data warehouse. The ETL tools will help developers to easily develop the data pipelines. Tools like Informatica, Abintio and many others hae GUI which will help with drag and drop functionality to create ETL pipelines. In modern data stacks spark can be used for creating complex data processing pipelines. Cloud providers like AWS also provided serverless ETL ( like AWS Glue) where data engineers can focus on the development of the code rather worry about infra stuff. This will improve the development life cycle and go to market quickly.

Lors de mon passage dans une entreprise de commerce électronique, j'ai introduit un outil ETL pour simplifier la gestion de nos données provenant de diverses sources. Grâce à cette solution, la consolidation des informations s'est améliorée, accélérant nos processus opérationnels. Cela a eu un impact direct sur la qualité des décisions prises et renforcé notre position concurrentielle en facilitant l'accès à des données précises et actualisées.

Enfatizo que a seleção depende das necessidades e capacidades de cada empresa. Existem várias ferramentas como por exemplo: Pentaho, Apache Hop e Informatica Power Center... mas aqui quero levar a reflexão, que a escolha da ferramenta de ETL deve ser feita com base em uma avaliação cuidadosa das exigências específicas da empresa, incluindo tipos de dados, volume, frequência de atualização, complexidade das transformações necessárias e, claro, orçamento e recursos disponíveis.

It depends on source / Target system and accordingly we can make right choice in discussion with stakeholders as many times they would like to go with current ETL as they don't need additional license. Reference: when working on real time data replication/ S4 HANA CFIN implementation: SLT is the first choice in many cases, there us another option Magnitude as well for certain S4 HANA. Another good option used for many legacy requirements/ SAP is Data Services (BoDS) as it could easily integrate with SAP, Azure Data lake and many other systems has good Transformation capabilities. In our project we have leveraged both SLT and BoDS for various requirements. Another possible options are : Informatica, Talend, and many more.

Essentially ETL process is helping to consolidate data from one or more data sources into a one, which has specific purpose. Important piece in this ETL process is T i.e. transform. Because to built a different system on top "transformed" data such as reporting, analytical or any decision support system needs data to be in specific form. Consolidated data store can be termed as data warehouse or data lake, which is essentially a data store that is re-purposed for analysis, reporting or fulfill further data science purpose.

I will consider Apache Hop (Pentaho's fork) Apache Hop stands out as an excellent cloud ETL migration tool for data warehousing due to its flexibility, user-friendly interface, and adaptability to various environments. It delivers robust performance and scalability for handling large data volumes, supports a wide range of data sources and formats, and offers cost-effectiveness as an open-source solution. Additionally, its active community and strong support system enhance its usability and adaptability for diverse needs.

Vou considerar o Apache Hop (Fork do PDI) O Apache Hop se destaca como uma excelente ferramenta de migração de ETL em nuvem para data warehousing devido à sua flexibilidade, interface amigável e adaptabilidade a vários ambientes. Ele oferece desempenho e escalabilidade robustos para lidar com grandes volumes de dados, oferece suporte a uma ampla variedade de fontes e formatos de dados e oferece economia como uma solução de código aberto. Além disso, sua comunidade ativa e forte sistema de suporte melhoram sua usabilidade e adaptabilidade para diversas necessidades.

ETL, there are a plenty of tools. But the factor on what clients want to use, is based on the current system, upgrade/migration cost, tool evaluation,maintenance and finally the most important overall budget.

ETL tools, whether On-Premise (e.g., Informatica, IBM DataStage), Cloud-Based (e.g., AWS Glue, Google Dataflow), or Open-Source (e.g., Apache Airflow, Talend), are crucial for effective data integration. On-Premise tools offer control but demand higher maintenance. Cloud-based tools provide flexibility but pose challenges in security and governance. Open-source tools offer innovation but may require advanced technical skills. Choose based on project needs, considering factors like control, scalability, security, and team expertise, aligning with unique data integration goals.

In market there is wide range of ETL Tools are present both in cloud and On Prem. Few of the market demand tools are Informatica Powercenter, Informatica BDM, IDQ, ODI, Talend, Datastage, Abinitio, IICS, Matillion, ADF, AWS Glue, Pentaho, etc.

It is very important to understand the business requirements, data sources (type of data and frequency) before choosing the ETL tool. Depending upon the batch or streaming applications, etl tools can be selected . As organization's are modernized their date eco system to cloud, factors like server less offering can be considered for the ETL. For example in AWS cloud for streaming Kinesis and MSK and for batch etl aws glue serverless can be used . Also for complex data pipelines EMR and spark data processing framework is a good choice . For ELT kind of architecture dbt with Redshift or dbt with snowflake is a better option where parallel processing and high performance of data warehouse server capabilities can be used.

Firstly you need to know the environment and what is the exact requirements of the customer, from SAP perspective, I've worked on SAP BODS Which consists of a UI development interface, metadata repository, data connectivity to source and target system and management console for scheduling of jobs. Being an SAP product, BODS has seamless integration with SAP systems, such as SAP Business Suite, SAP BW (Business Warehouse), and SAP HANA, which enables organizations to optimize existing SAP investments. SAP SLT Transformation Rule in HANA Jobs can be scheduled and monitored easily with SAP BODS It enables users to maintain and document metadata. The jobs created in BODS can be both real time and batch job.

Choosing ETL tool or appropriate technology is dependent on several factors such as - what is underlying current architecture, technology stack of product for which ETL solution extension needs to be built, what type of goal is aimed to achieved by using ETL solution, also in which phase the product lifecycle current product is?. Consideration of all these factors will lay foundation to build technology roadmap for implementation. Apart from this strategic viewpoint other tactical viewpoints are also crucial such as what is underlying data relationship and what transformation logic needs to be written. When transformation supposed to trigger. E.g. ETL process should transform and load data in real time or need is have close to real time?

Multiple factors are weighed in and taken into consideration while choosing an ETL Tool. Few of them are: - Scalability of the project - Ease of use - Feasibility of product support offered by the product vendor - Data handling Capacity

Consider scalability for future growth, prioritize ease of use, and assess compatibility with existing systems. Factor in performance, evaluating speed for real-time or batch processing. Balance costs, including licensing and support fees. Opt for flexibility to handle diverse data formats and structures. Prioritize security features, ensuring compliance with standards. Check for strong community or vendor support and regular updates. The ideal ETL tool aligns with current needs while accommodating future expansion.

Below are some best practices for using ETL tools in the context of data integration and data warehousing. 1) Define the goals and scope of your ETL process, understand business requirements and objectives that the ETL tool is intended to address. 2) Gain a deep understanding of source systems, including data formats, structures, and extraction methods as this is crucial for designing effective extraction processes. 3) Test individual jobs/transformations to ensure correctness and functionality. Additionally, test ETL process end-to-end data pipeline as a whole. 4) Implement monitoring and alerting mechanisms to identify and address issues within ETL processes. This helps to maintain health and performance of ETL system.

One thing I have found helpful with best practices they are good for winning client confidence and avoiding any arguments. If you documented them and it minor updates over the period most of those best practices are still fit well in majority of projects. Good understanding helps in brainstorming exercises with business

In my experience navigating the ETL landscape, a crucial aspect often overlooked is the alignment of ETL tool selection with specific project requirements. Beyond the general considerations, it's vital to assess the scalability, adaptability, and ease of use of the chosen tool within the unique context of your organization. For instance, understanding the volume and variety of data to be processed, as well as the need for real-time integration, can significantly impact the choice of an ETL solution. My recommendation is to not only focus on immediate needs but also anticipate future scalability requirements and emerging data trends to ensure the selected tool remains effective in the long run.

Even though ETL provides a great control over the flow of the data, it's always good to consider what kind of source and target are we using. Can you use bulk or stream loading/pulling? Checking the through put of jobs, checking the dependencies over one another. These are some best practices.

Data Modelling design & documentation in place. Proper Naming convention for all transformation & derived columns. ETL logs should be maintained for trouble shooting & debugging purpose. Parallel ETL design or modularity design in place to increase performance tuning. Audit logs to check row count between data source & data sink. Recovery point in place so even after failure the job should continue from where it got interrupted.

ETL/ELT are great ways to pipeline data into a physical storage like a datalake or datawarehouse. There are however other alternatives like having a logical data warehouse where you can integrate data virtually in real time without having to move the data physically but yet achieving the same effects of a combined semantic layer. Of course, there are still benefits to moving data physically via ETL/ELT, so it really depends on the use case.

ELT is the new favorite. Using open source tools like Airbyte and dbt can dramatically improve efficiencies while keeping costs down. Pair Airbyte with a local cloud provider that doesn’t charge for bandwidth costs for a killer combo for maximum data refreshes without killing the bank.

Before deciding on a tool, you should consider whether an ELT or an ETL process is more suitable for your requirements. Even if ELT and ETL sound similar, their application is different. The main difference between ELT and ETL is the order of operations between the two processes, which is why they are better suited to different situations. Other differences lie in the data size and data types that each process can handle.

Effective ETL management demands astute consideration of costs and resources. Financial implications involve licensing fees, cloud service expenses, and potential infrastructure investments. Balancing these against budget constraints is critical. Efficient resource allocation is equally vital, ensuring computing resources align with data processing demands. Scalability options should be explored to accommodate future growth without exponential cost increases. Ongoing maintenance, updates, and monitoring contribute to the long-term viability of ETL processes, adding to the overall costs. Striking the right balance between cost-effectiveness and resource optimization is pivotal for the sustained success of data integration endeavours.

Approach is important before you select any tool. Once it's clearly documented it becomes easy to answer some important questions and you should be able to fit more than 1 option of ETL tool in your architecture. This will help you save time on multiple ETL tool reviews. These days vendors are providing help to see if their ETL tools fit your requirements. Lots of time for your development team experience also becomes a crucial point in decision making. You surely don't want to choose a tool they don't know.