Was ist der effizienteste Weg, um Daten aus zwei Datenbanken zu integrieren?

One time at work, when tasked with integrating data from two databases, in my experience, employing Extract, Transform, Load (ETL) processes has proven highly efficient. One thing I’ve found helpful is using ETL tools like Apache Nifi or Microsoft SSIS, which facilitate seamless extraction of data from source databases, transformation to meet the target schema, and loading into the destination database. These tools often offer visual interfaces for designing workflows, making it easier to map data fields, apply transformations, and handle any necessary data cleansing. ETL processes not only streamline data integration but also provide a structured approach for managing complex mappings and ensuring data quality during the transfer.

In my experience before the integration, the success starts with an adequate vendor selection evaluate the capabilities of both databases, limitations and establish the purpose of the integration to evaluate if it's a good match, make a plan and document it to define scope of the parties, action plans in case something happens and points of contact to resolve things.

Integrar dados de dois bancos de dados requer analisar fatores como tipo, frequência e ferramentas dos dados. Técnicas comuns: integração em tempo real (replicação ou canal de dados) ou em lote (ETL ou sincronização de dados). Ferramentas: MuleSoft, Informatica, IBM InfoSphere ou códigos personalizados. Considerar a segurança, desempenho e escalabilidade. Escolher a solução mais adequada ao projeto.

In my experience, the most efficient way to integrate data from two databases leverages the lakehouse architecture. This modern pattern helps ensure we don't have stale data downstream and consolidates all data in the data lake. For example, the data from these databases could have different refresh cycles, and if we built ETL pipelines to our BI tool differently, we have 2 failure points. But if we consolidated the data in the lake, we have a central source for data.

Assessing the requirements is the very first and important step here. It’s important to know what is the objective behind, what is the impact of the change and how to handle the impact.

There are so many methods; I would advise selecting in a data-driven and contextual way. Think about 'data volume' and 'velocity' - ETL might be suitable for smaller batch loads, while ELT for high-volume or rapidly changing data. Then, we have 'data complexity' -ETL's upfront processing might be better for complex transformations, while ELT's flexibility excels in handling diverse or evolving structures. What about your target systems - If performance is critical, consider real-time data integration tools for seamless ingestion. Keep 'latency' in mind - Low-latency applications might favor CDC (Change Data Capture) for targeted updates, while ETL/ELT are suitable for periodic refreshes.

ETL - Pros: - Cleaning and validation enabling quality - Enables clean data in the warehouse - Offload processing from source systems - reducing load on transactional systems ETL - Cons: - Latency causing a delay in data availability - Designing the ETL process is complex for large data sets - Dependancy on source systems ELT - Pros: - Low latency increasing availability of data in the warehouse - Scalability to leverage processing power and parallel processing - Real-time data processing - Easy design and maintenance ELT - Cons: - Data quality concerns limit transformation - Resource requirements on the warehouse for complex transformation and cost

The most common techniques for data integration are: Manual Data Integration: Manually write code that moves and manipulates data based on needs. Suitable for small-scale data integration tasks. API-based Integration: Applications are directly linked and move and transform data based on event triggers. Appropriate when data changes in real-time and integration needs to be triggered by specific events in applications. Common Data Storage: Data is extracted from sources and stored in a data lake or warehouse providing a single source. Data Virtualization: Data from different sources is combined in a virtual database. Useful when real-time access to combined data is critical without physically moving or storing it in a central repository.

In the end, the ways to integrate data are limitless. You should consider a number of factors: 1) Available tools 2) Automation capability 3) Manual involvement 4) Validations required 5) How the data needs to be reported

Select an appropriate integration method based on your needs. Options include ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), or real-time integration. Each method has its strengths, so choose the one that best fits your data and workflow requirements.

Before trying to pick the right tool, clarify your integration goals. Do you need real-time synchronization or batch processing? Is data volume a major concern? Define the source and target databases, their schema compatibility, and desired transformation complexity. Now, map your technical landscape. On-premises or cloud-based deployment or hybrid? Existing integrations or standalone solution? Budgetary constraints? Assess your team's skillset and available support for the chosen tool. Next, compare available integration tools based on your criteria. Consider factors such as connectivity, data transformation, scalability and performance, security and compliance, usability and support.

Once the integration method is determined, select a reliable tool that supports the chosen approach. Tools like Apache NiFi, Talend, or Microsoft Azure Data Factory offer comprehensive features for data integration across diverse databases.

The "best" tool depends on specific requirements and constraints. It's better to conduct a thorough evaluation, possibly through proof-of-concept projects, to determine which tool aligns best with the organization's needs for integrating data from two databases. Consider the complexity of your data integration needs. Some tools are better suited for simple data transfers, while others excel at handling complex transformations and large datasets. Determine whether your integration requires real-time or batch processing. Some tools are designed for near real-time data integration, while others are more suitable for periodic batch processes. Assess the scalability requirements of your integration.

Before starting the integration of multiple databases one question must be asked: Why? Why is the integration needed? It's going to be used by a single program or multiple ones? Only after assessing the why we can think on how.

Choosing the right tool for data integration is crucial and should depend on the specific requirements of your project. Consider factors such as real-time synchronization, data volume, complexity of transformations, as well as on-premises or cloud-based requirements. Don't forget to assess the capabilities of your team and the support available for the tool you choose. Tools such as Apache NiFi, Talend or Microsoft Azure Data Factory offer comprehensive functions for the integration of different databases. A thorough analysis of these aspects will ensure an efficient and effective integration that is both technically feasible and strategically useful.

It's important to remember who will be the final user of the database (the person involved to enter the data or feed the database). User Acceptance Testing needs to involve different eyes with different profiles to cover all possible situations, testing also should involve the different roles if applicable. Create a testing plan or testing checklist to validate and document the scenarios tested, this also functions as a tool to communicate with the programmers and have the same traceability with all integrations.

Prioritize data accuracy and completeness by employing data validation rules, comparing record counts, and verifying data types. Go beyond the numbers and scrutinize data consistency. Check for duplicate entries, inconsistent formatting, and potential discrepancies in values across related fields. Don't neglect performance monitoring. Track processing times, identify bottlenecks, and optimize data transfer speeds. Embrace automation with real-time alerts and dashboards to visualize anomalies and catch issues. Periodically review your methods, adapt them to data schema changes, and remain vigilant for potential integration drift.

Before implementing the integration at scale, conduct thorough testing to identify and resolve potential issues. Establish monitoring mechanisms to track data flow, detect anomalies, and ensure the reliability of the integrated system.

There are several factors to consider to effectively integrate data from two or more databases, including the type of databases involved, the volume of data, the desired level of integration, and the specific use case. Here are some common methods I have used: 1. ETL (Extract, Transform, Load) Processes: One of my go-to methods when doing data warehousing because of its ability to handle large volumes of data. Using custom ETL tools to extract data from one or more disparate source databases, transform it as needed, and then load it into the target database. 2. Database Linking or Federation: Some database systems (like MSSQL) allow you to establish links between them, enabling queries referencing tables from other databases.

Implement change data capture mechanisms to identify and capture changes in source database. The goal of CDC is to track modifications, additions and deletions and efficiently propagate those changes to a target database. Capture changes to individual rows, new rows or deleted rows.

Small Footprint. Federated Query Engine like Trino, Dremio etc. No data Movement. Doing classical ETL. Possibility to do complex transformations, and apply business rules CDC, change data capture to bring data to a multi purpose storage

The method will depend on different factors including the type of database, the volume, frequency of updation. My preferred methods are listed below: ETL method i.e Extract Transform and Load using tools or We can link the two databases by running queries. Further, there is data virtualization i.e if we can create a layer on top of the two databases and run queries to eventually consolidate them for the viewer. Then there is no need to merge the two databases

Continuous optimization is crucial. Analyze performance metrics and identify bottlenecks. Optimize queries, indexing, and data structures to enhance efficiency. Regularly review and update the integration process to adapt to changing business needs.

Utilizing ETL (Extract, Transform, Load) Process. Use ETL tools to extract data from both databases, transform it into a consistent format, and then load it into a central database or data warehouse. You can also try API integration, if the databases support it, using APIs for real-time data integration.

Optimisez et améliorez continuellement le processus d'intégration de données en utilisant des analyses et des feedbacks pour identifier les problèmes et les opportunités d'amélioration.

This is easier said than done. One challenge we’ve seen with our clients at Sorrek is when they attempt to ingest data from a third party vendor like Crunchbase or Pitchbook into a CRM like Salesforce. Duplicates aren’t always records that have similar data - you can have multiple companies called Compass for instance. Duplicates in this case would be records that represent the same entity. Take for example Gusto and Zenpayroll, two seemingly different names that actually belong to the same company. Using this approach to mapping and duplicate resolution will help improve data quality and prevent tech debt, duplicates, and other headaches in the future.

Vamos de reflexão... Costumo comentar que a maneira mais eficiente de integrar dados de dois bancos de dados depende do contexto do negócio. Avaliar as necessidades é fundamental. Considere o objetivo da integração, a frequência de atualização dos dados, a quantidade de dados a ser integrada, a complexidade das transformações e validações necessárias e como é desejado acessar e usar os dados. Este passo irá guiá-lo na escolha do método e da ferramenta mais adequados para o seu projeto. Como sempre digo: Não existe uma "melhor" ou "solução mais eficiente" universal para a integração de dados; o que importa é encontrar a solução que melhor atenda às necessidades específicas do negócio.

Data is ony as good as having people who can properly interpret the data in what can be multiple contexts. Data is not the solution, just part of good decision making.

In the data tapestry, an integrator embarked on a narrative of merging worlds seamlessly. * API orchestration became the bridge, facilitating harmonious communication between two databases. * ETL transformations played the role of translators, ensuring data spoke a common language. * Incremental loading emerged as the storyteller's guide, optimizing efficiency by only updating changed data. This narrative unfolded as a strategic symphony, where API orchestration, ETL transformations, and incremental loading converged, creating an efficient integration storyline that allowed data to flow effortlessly between the interconnected databases.

Consider factors such as data security, compliance with regulations, scalability, and the potential impact on existing systems. Ensure that the integrated solution adheres to industry standards and aligns with your organization's long-term data strategy. Regularly revisit and update your integration strategy to stay agile in the face of evolving business requirements.