What are the best data integration platforms for data engineering?

Few more interesting aspects are: Real-Time Processing: Many of these platforms support real-time data processing, which is crucial for applications that require immediate insights, such as fraud detection or personalized recommendations. Data Governance: They often come with robust data governance tools that help organizations manage data access, ensure data quality, and comply with regulations. Built-in Security: These platforms usually have built-in security measures, including encryption and identity management, to protect sensitive data. Disaster Recovery: They often include disaster recovery capabilities, such as automatic backups and geo-replication, to ensure data availability and durability.

There are multiple tools/technologies available in the market which makes the integration very easy. Before integrating your system with any external server/system we should make sure the system we are trying to integrate should be well maintained in terms of security & the quality checks. The integration could lead to the overall system failures/attack if it is integrated without good checks. To integrate the system we have multiple platforms/tools available such as IICS i.e the cloud platform which is still market leader & user friendly ingestion tool. It provides a wide range of integration strategies which can be used & utilised well. There are plenty of other tools available which can be used such as Talent,nifi etc.

Choosing an integration platform depends on various factors such as the specific needs of the project, scalability requirements, budget considerations, and the existing technology stack if any. Whilst there isn't a one-size-fits-all solution several platforms such as Apache Kafka, Apache NiFi, Talend, Informatica, Microsoft Azure Data Factory, AWS Glue and Apache Flink amongst others are widely used in various organizations. Each platform comes with its pros and cons, and the best choice often depends on the uniqueness of the project, it's crucial to evaluate factors like ease of use, support for diverse data sources, security, scalability, real-time processing capabilities if required and community support when making a decision.

Since the popularity of cloud uses at scale,the use of cloud data warehouses is no brainer and there is a lot of migration from on prem to the cloud data warehouses. And when it comes to choosing one,the first attribute that plays a vital role is the data platform being cloud agnostic and that's where snowflake wins over redshift and BigQuery. But again that is use case based decision if you have your entire data and infrastructure in Amazon and using glue etc it makes more sense to go with redshift. Snowflake and other such platforms offer full SaaS experience with easy scalability,cost efficiency and provisioning. And you club your cloud data warehouses (data lake) to any ELT tool to make analytics ready data (data warehouses etc.)

We always have a choice when build a data solutions: - Use native cloud tools like Glue, Data Factory, Data Fusion - Use managed services like Airflow on Data Factory, Managed Airflow on AWS and so on - Use self-hosted open source tools like Airflow, Dagster, Meltano - Use 3rd party tools like Prefect Cloud, Meltano Cloud, Fivetran It depends on the budget and team skills.

In general; Apache foundation have several good products related data environment to use: ?? Data warehousing: hive, doris or kylin. ?? Processing: spark, beam and flink. ?? Database: druid, ignite and drill. ?? Queue: Kafka. ?? Formats: hudi, iceberg and arrow. A great point that all tools can be adopted for any data teams in any cloud provider.

Having open-source platforms would be an advantage if your company needs flexibility from vendor lock-in risk. But the best tool for open-source would depend on your company's mission and strategic imperatives. Here's the list of open-source tools concerning the business case that it may solve: ETL Orchestration for reports generation: Apache Airflow Real-Time Transaction Monitoring: Apache Kafka Open Source Data Warehouse: Apache Iceberg Big Data Data Wrangling: Apache Spark This is just a short list, however, subscribing to open-source would have tremendous benefits for the company such as the following: - Ease of finding talent - Vendor lock-in risk mitigation - Community updates

Hadoop: Distributed storage and processing for big data. Spark: Fast analytics engine for large-scale data processing. Kafka: Real-time streaming platform for building data pipelines.

Data integration brings together information from various sources and systems, providing a unified and comprehensive view. There are some widely used data integration platforms. 1-Apache NiFi 2-Apache Kafka 3-Apache Spark 4-Microsoft Azure data Factory data engineers are responsible for building a robust integrated data infrastructure for an organization.

Using open source tools for data integration is a cost effective and flexible approach, supported by collaborative communities. These tools offer customization, ensuring adaptability to unique business needs, and prioritize interoperability for seamless integration. With transparent source code enhancing security, open source reduces vendor dependency risks, allowing greater control. The scalability of these tools accommodates growing data volumes, and their rich ecosystems facilitate integration with other open-source projects. Embracing open-source data integration provides a dynamic and responsive foundation for evolving data needs.

The best of both worlds can be found in hybrid data integration platforms. These platforms blend cloud-based and on-premises capabilities, allowing you to leverage existing infrastructure while taking advantage of cloud features. Take Microsoft Azure Data Factory, for example, which combines the strengths of both environments.

La tendance est : - au code (pySpark / DBT) - à l'évolutivité - au serveless - à la capacité de faire à la fois de l'ETL et de l'ELT - au capacité de parallélisation

Nifi is one of the open source options available to be cost effective. Commercial tools can be leveraged in case of complex ETL transformations and master data management requirements like MDM, master data referential etc. Products like Denodo, Data bricks can be leveraged in case you have multi cloud environments and want to build a data fabric for platform integration

IBM Cloud Pak for Integration is a hybrid integration platform that uses closed-loop AI automation to support various integration styles. It offers a complete set of integration tools in a unified experience to connect applications and data across any cloud or on-premises environment.

Hybrid platforms in data engineering are systems that combine the advantages of different data processing paradigms, such as batch, stream, and interactive. They enable data engineers to handle diverse and complex data workloads efficiently and flexibly, while reducing the operational and maintenance costs.

Choosing the best platform for your needs is always a tricky thing since there are so many new tools in the market but it always comes down to below question: 1. What is the budget for maintaining the tool? 2. Does the existing team have a skillset to manage the tool after its build? 3. How much of money/time it will help me save (ROI)? 4. How easy it is to switch in the future if required? 5. Scalability? 6. Flexibility? 7. Set up cost? 8. Do we need external support going forward to update it?

To find the right data integration platform for Data Engineering, first, figure out what you really need. Make sure it can handle your data amounts, is easy to use, and seamlessly connects with your data sources. Think about costs, security, and support. Check if it's flexible for different jobs and future-ready. Give it a try with a small project, and make your choice wisely.

Le choix d’une plateforme d’intégration de données doit en effet prendre en compte plusieurs critères tels que (liste non exhaustive) : -La couverture des cas d’usages -La connectivité : connecteurs natifs, connecteur API,… -La facilité d’utilisation : Low code et assistant de développement versus Full code, plateforme intégrée ou assemblage de différents outils indépendants ( ingestion, transfo,…) -ETL ou ELT ? -Facilité d’administration -Sécurisation de la plateforme: gestion des r?les, chiffrement,tra?abilité,… -Disponibilité des compétences sur le marché -Editeur : maturité de la solution, roadmap innovation,réactivité du support -Co?t : ma?trise et suivi des co?ts,effort de setup de la plateforme, de formation et de contractualisation

You need to define the criteria for your evaluation. You can buy a license for a data engineering tool for a huge amount of money to be paid every year for the services or you could use something like Azure Data Factory that works with Pay As You Go mode. Charging you for data in out. For a couple of hours of pipeline usage per day you might pay around 250 USD per month. Or you could use python code to create your data pipelines that could be free of charge. The functionality, out of box features, alerts, notifications, rules, reusability, compliance features, security, high availability, analytics about your job executions, etc are the things that can come our of the box for good tool or you write it on your own in case of python.

I prefer Azure Data Factory, a cloud-based data integration service offered by Microsoft, since it has several advantages: Simplicity and Scalability: Azure Data Factory provides a fully managed, serverless data integration service that simplifies hybrid data integration at an enterprise scale. Wide Range of Connectors: It offers more than 90 built-in connectors for ingesting data from various sources, including big data sources, enterprise data warehouses, and SaaS apps. Code-Free ETL Processes: You can easily construct ETL (extract, transform, and load) and ELT (extract, load, and transform) processes in an intuitive environment without writing code.

It is always better to create your own data ingestion tools if you have the bandwidth. This gives us very low-level customizability where you can choose where to deploy, and choose what tools to use at every stage and the point is your data doesn’t go to a third party. If bandwidth is the issue then there are multiple tools open source and paid you can always use. That will help in reducing your resource cost and time.

- Ensure the platform adheres to security standards and provides features for data encryption, access control, and compliance. - Evaluate the strength of the platform's community for troubleshooting, sharing best practices, and staying updated on new features. - Check if the platform supports AI capabilities for enhanced data processing, analysis, and decision-making. - Choose a platform that can adapt to evolving technologies and business requirements, ensuring long-term viability.

What's the role of a Data Engineer? Data engineers play a critical role in any organization that relies on data to make decisions. They design and build the infrastructure that enables data to be collected, stored, and processed effectively, ensuring that organizations can extract insights from their data and make informed decisions based on that information.

Ingestion tools are the starting point for data engineering, sometimes it takes much time to integrate with the existing system and takes even longer to bit the CDC, if you have time and resources to invest then building your ingestion tool is a good option but reduce your the time and focus the resources on value creation work it better to use an intention tool like Fivetran or Hevo Data.

You should also look at - Support network for the tool, do they have a good pool of engineers in case you are stuck with some issues? - Training and documentation, do they have an academy to get your resources trained, ILT, self paced, free or paid? - Community, do they have a good community where you can interact with other users of the tool to learn and get support from experience? - Certification, do they have a good certification program so that you can rely on resources that are already certified? - Customer forums, do they support customer forums for you to learn and get inspired from other customer use cases?