You’re trying to integrate data from multiple sources. What tools can help you do it right?

Para aqueles que buscam ter controle total do processo recomento usar python e suas diversas bibliotecas, do contrário caso queria uma ferramenta lowcode recomendo Informática PowerCenter, Talent ou JETL (essa ultima desenvolvida no Brasil)

Data integration platforms are a critical piece of the Data & AI puzzle. I've worked my way through hadoop, cloud (all providors), databricks. Often, a platform is chosen without analysing the business outcomes from the platform. However, from my experience the choices should be made very pragmatically. Decisions of real-time vs batch are also often overlooked. A platform exist to drive business and growth by enabling interoperability between systems at scale. One fit all rarely exists but just best of breed doesn't work. The sweet spot is between foundational platforms and best of breed to address mission critical use-cases and data gouveranance.

Check keboola.com. They have everything. It's a Palantir for commercial sector. There are gazillions tools that you can out together, create governance around that, security, logging etc. or use Keboola a be done. Quickly.

Para que seja possivel a integração é necessário contar primeiramente com uma boa ferramenta de data discovery para ter a visibilidade correta de quais são todas as fontes de dados existentes. Etapa de data cleasing e normalização dos dados são fundamentais para eliminar duplicidades e redundâncias antes da integração de fato. Ferramentas de ETL (DBT, Data Migration) adequadas ao volume de dados e processamento necessários são mandatorias para o sucesso do cenário.

For seamless data integration from diverse sources, employ robust tools like Apache NiFi for data flow automation, Talend for open-source integration, and Microsoft Power Query for Excel. Additionally, consider using Informatica PowerCenter for enterprise-grade data integration and Apache Kafka for real-time data streaming. These tools facilitate efficient data consolidation, transformation, and synchronization, ensuring a cohesive and accurate integration of information from various sources.

I work with a few tools, but SAP Data Services is in my estimation one of the best, especially when working on a SAP project. It can integrate multiple platforms and ERP's and it is very flexible. Informatica is another great tool, albeit the learning curve can be steep. Syniti ADM is also very good but has a steep learning curve.

The choice of tool depends on specific use cases, the context in which they can be used, and the nature of the data sources. 1. ETL (Extract, Transform, Load) Tools are most commonly used for getting data from multiple sources 2. Change Data Capture (CDC) tools can be used for data integration. CDC is a data integration method that involves data streaming incrementally at shorter intervals to a data warehouse or lake to enable near real-time operational reporting 3. Streaming tools involve capturing, processing, and routing of real-time data from various sources to target systems. 4. ELT Tools are similar to ETL tools, however, the framework differs by having to ingest data into a warehouse or lake as it appears in the sources.

While there are surely more efficient tools, I've been pleasantly surprised by the Power Query Editor built into Excel. You can import data from a wide variety of sources (APIs, Sharepoint, SQL), transform it, and have the result turn into a self-updating Excel file you can share with your colleagues. No need to export as a last step.

Data Integration means getting data from multiple sources with different formats. So, any tools which is compatible with your sources and allows you to transform the data during processing or before load could be the best solution. There are multiple tools to achieve same. There could be tools which do not allow direct access to source but there are some software or additional build in app purchases which can help to achieve it. Like for API connection or Salesforce or Google analytics connection, which are not directly possible in SSIS, you can use software like CDATA, SSIS Salesforce Source Connector(CRM) and more. So we need to first identify the sources, then the tools which can help to achieve same, and project budget.

Apache Kafka: A distributed event streaming platform that enables real-time data integration. It is scalable and fault-tolerant, making it suitable for handling large volumes of data. Apache Nifi: An open-source tool for automating the flow of data between systems. It provides a web-based interface for designing data flows, making it user-friendly.

Dell Boomi: A cloud-based integration platform that allows organizations to connect applications, data, and people. It supports various integration patterns. Jitterbit: An integration platform that allows users to design, deploy, and manage the integration of applications and data. It supports both on-premises and cloud-based integrations. IBM InfoSphere DataStage: A data integration tool that enables businesses to design, develop, and run jobs that move and transform data on-premises or in the cloud. Airflow (Apache Airflow): An open-source platform to programmatically author, schedule, and monitor workflows. It can be used for orchestrating complex data pipelines.

Apache Spark: A fast and general-purpose cluster computing system for big data processing, often used for ETL tasks. Google Cloud Dataflow: A fully managed service for stream and batch processing of data. TIBCO Data Virtualization: Provides a unified data access layer across disparate data sources. MuleSoft Anypoint Platform: Provides tools for API creation, management, and integration. Amazon Redshift: A fully managed data warehouse service in the cloud. SAP Master Data Governance: Helps ensure data integrity and consistency across heterogeneous systems. Python: With libraries like Pandas, NumPy, and others, Python can be used for data integration tasks. SQL: For database integration tasks

In my experience, I've found data integration frameworks to be incredibly valuable. These are like roadmaps that guide us in designing and implementing our data integration strategies. One framework that has been particularly helpful is the Data Management Body of Knowledge (DMBOK). It helps us define our data integration goals, scope, and architecture, ensuring consistency and quality across our data sources. By following its guidelines and best practices, we can align our data integration efforts with our business objectives and measure our outcomes effectively. Other frameworks like the Common Data Model (CDM) and the Data Integration Maturity Model (DIMM) also provide useful insights and direction in our data integration projects.

Indeed, a data integration framework provides a structured approach to designing and implementing data integration strategies. It helps organizations define their goals, scope, architecture, methods, standards, and policies to ensure alignment with business objectives and consistent data quality. Examples such as the Data Management Body of Knowledge (DMBOK), the Common Data Model (CDM), and the Data Integration Maturity Model (DIMM) offer comprehensive frameworks to guide organizations through their data integration initiatives, facilitating efficient and effective management of data assets.

Frameworks like Apache Spark or Apache Beam offer scalable and flexible frameworks for processing and integrating large volumes of data across distributed computing environments.

Following tools can be used to integrate data from multiple sources: AWS Glue: a. It helps get your data ready for analysis by easily preparing and loading it. It's fully managed, so you don't have to worry about the technical details – just focus on turning your data into valuable insights. Apache Kafka-It is like a super-efficient messenger for your data. It helps send and receive messages between different parts of your computer system in real-time. Think of it as a highway for your data, making sure it gets to where it needs to be quickly and reliably. It's great for building applications that need to process data as it happens.

A collection of tools, procedures, and technologies known as "data integration services" make it easier to combine data from many sources into a single, cohesive picture for analysis, reporting, and decision-making. In today's data-driven business world, where organisations gather data from various sources like databases, applications, APIs, cloud services, and more, these services are crucial. 1. Data Extraction 2. Data Transformation 3. Data Loading 4. Data Quality Management 5. Metadata Management 6. Data Governance and Security 7. Data Replication and Synchronization 8. Workflow Orchestration 9. Scalability and Performance Optimization

These services encompass expertise in various facets, including data analysis, modeling, mapping, quality assurance, governance, and security. By leveraging these services, organizations can benefit from expert guidance, overcoming technical and organizational challenges inherent in data integration. These services often align with best practices and utilize appropriate tools to ensure the success of integration projects. Well-known providers in this space, such as Accenture, IBM, and Deloitte, exemplify the industry's commitment to delivering comprehensive and tailored solutions for effective data integration across diverse business needs.

Data integration services play a crucial role in navigating the complexities of integrating data seamlessly. From planning and executing to ensuring data quality and security, these services offer invaluable expertise and support. Many firms are providing tailored solutions to tackle diverse integration challenges. With their guidance, businesses can optimize their data strategies and unlock the full potential of their data assets. There are many players like, #InformationManagementServices, #Cognizant, #Hexaware etc. just choose the right one !!

Absolutely, a data integration service offers professional expertise and support throughout the data integration process, from planning to execution and ongoing maintenance. These services provide guidance on various aspects such as data analysis, modeling, mapping, quality, governance, and security, helping organizations leverage best practices and tools to achieve their objectives. Companies like Accenture, IBM, and Deloitte are examples of organizations that offer comprehensive data integration services, assisting clients in overcoming technical and organizational challenges to achieve successful outcomes in their data integration projects.

APIs are very powerful tools that a company should add to toolsets, including the integration of powerful AI tools, which themselves can be used for the purpose of data integration (reliability of this varies at the moment though, as specific models can be better-suited for one dataset over another). REST is certainly the most common and easy to use API. It is often argued that SOAP is necessary for complex and security-focused cases, but for the most part REST can also be secured using Oauth and HTTPS, and is simply more powerful in all the right ways for many users.

Event-driven architecture with Kafka event broker Event-driven architecture enables loose coupling of producers and consumers via event streams, and is a central part of microservice architecture, but can be used in a broader sense. The event streams provide a mechanism of asynchronous communication across the IT-landscape, where each participating service can be independently created, scaled, and maintained. Event-driven architectures are resistant to the impact of sporadic service failures, as events can simply be processed when the service comes back up. This is in contrast to REST API / HTTP communication, where a request will be lost if the server fails to reply. Apache Kafka is one of the most commonly used event brokers.

En general debe preguntarse cuantos datos integrará, de que tipo (estructurados o no), de cuantas fuentes diferentes, con que periodicidad (batch, real time), que necesidad de procesamiento o transformación necesita, y cual será el uso de la data (procesos operacionales, análisis descriptivo, predictivo). Con todas esas preguntas respondidas estará en condiciones de seleccionar una arquitectura de datos apropiada, que podría ser implementada con una o más tecnologías o herramientas. En general como base tendrá que escoger una tecnología de etl (o con enfoque elt si piensa en la nube), una de almacenamiento y una de procesamiento.

In my experience data integration is done smoothly with APIs especially if the sources where the data is coming from are different and also if the data are of different formats

API's serve the purpose it's not just automatic but it can do self validation filter the bad entries, create a report for resolution, provide additional security, easily integrated with multiple or most available systems in current practice. Most importantly it can easily revert the data on dashboards to earlier dates which saves lots of time to identify the data issues from source systems

La integración de datos es una tarea crucial en el panorama actual, donde la información surge constantemente desde diversas fuentes y en distintos formatos. En este contexto, los scripts de integración de datos se posicionan como herramientas esenciales que ofrecen la capacidad de adaptarse y resolver las necesida des que surgen en este proceso. Lo mejor de los scripts es su versatilidad y capacidad para abordar situaciones tanto simples como complejas. Desde la automatización de tareas repetitivas hasta la manipulación de datos provenientes de fuentes heterogeneas, como el raspado web, estos scripts se convierten en aliados indispensables para los profesionales de la gestión de datos.

Integrating data from various sources to create a single, cohesive perspective is known as data integration. The method and methods used may vary based on your unique needs and the technologies you're working with. like python frameworks, SQL, java, Scala, R, powershell, Linux scripting as strong instruments to guarantee smooth and efficient integration between various resources.

Indeed, data integration scripts play a pivotal role in customizing, automating, and handling specific data integration tasks. They are essential for scenarios involving non-standard interfaces like web scraping or file parsing. Scripts, written in languages like Python, SQL, or PowerShell, empower users to tailor integration processes according to their unique needs. These scripts enhance performance, efficiency, and accuracy in data integration by allowing for fine-tuned control over operations. Whether manipulating data structures, transforming formats, or bridging disparate sources, data integration scripts serve as powerful tools for ensuring seamless and effective integration across diverse datasets and systems.

Hopefully, there should be a minimum full code scripts and any which are used must be orchestrated with some kind of pipeline dependency tool.

Data integration scripting is a tool that can be used in conjunction with APIs to automate processes, including parsing/transforming data for whatever specific use you may have. A single script could call several different API functions, compare datasets, etc. It is suggested that anyone conducting data integration utilize both of these tools to maximize their efficiency, especially in cases where real-time data analysis is critical.

When trying to integrate data from multiple sources, it would be important to extract the metadata from the sources and also from the transformation logics used during integrations (#ETL logics). Such metadata would be critical to develop the system of record (#SoR) for #metadata, which would enable development of automated lineage, application of Generative AI/LLMs in data management. Consider #Informatica #IDMC (Intelligent Data Management Cloud), which could enable you with all the above capabilities in one unified (yet modular, individual services) metadata-driven #datamanagement #SaaS platform, to derive business benefits now and also in the future.

Tools aren’t the problem. Vendors trying to hold their customers captive by making data integration difficult is the major issue.

best idea is to ensure compatibility with existing systems, scalability for future data growth, security measures for data protection, and adherence to regulatory compliance standards during the data integration process.

Data conversion and ingestion can strain overhead and sometimes become unmanageable without a great data conversion and management platform. I wish I had BettrData.io when I was building co-op databases. Check them out!

Any type of data analysis relies on the data source to be accurate. Junk in, junk out. So, it is paramount in any organization to establish good practices in this regard. Automate what you can. Give people drop-downs instead of free-form text fields. If you must use free-form text fields, periodically audit the input using Python or another tool to see if there are patterns. If there are patterns, for example certain words being repeated all the time, that is a cue nudging you into the direction of making this a drop-down instead, for example. Make certain fields mandatory. Calculated fields may not always calculate correctly, so keep an eye on those. And most importantly: strategize before you blindly put in yet another custom field.