Today's data often comes from file systems, data lakes, or repositories. In order to meet various business needs, we must integrate the data with the recording systems of other data sources to support analysis, customer-facing applications, or internal workflows. And this brings up a new problem-how do we choose the right
data integration tool to summarize all kinds of data? Today's article will discuss this.
The data is not in a database, file system, database or repository. In order to meet many business needs, the data must be integrated with the recording system of other data sources, and then used for analysis, customer-facing applications or internal workflow.
1. The huge market of data technology and functions
The question is: what tools and practices are used to integrate the data sources? What platforms are used to automate the operation of the data? Which tools are being commissioned for data scientists and data analysts to work harder when using new data sources? When using APIs for trading applications, can effective development and development tools enable faster application development?
Because many organizations have different types, quantities, and speeds of data, and have different business needs over time, there may be different methods and tools for integrating data.
2.
Data integration programming and script
Script is a shortcut for moving data, but it is not considered a professional-level data processing method. To become a production-level data processing script, it needs to automatically execute the steps required to process and transfer data, and handle multiple operational requirements. For example, if the script is processing large amounts of data or fast-moving data, you may need to use Apache Spark or other parallel processing engines to run multi-threaded jobs. If the input data is not clean, the programmer should enable exception handling and kick out the record without affecting the data flow. Programmers should also perform important calculation steps to facilitate debugging.
3. Traditional extraction, conversion and loading (referred to as ETL) tools
The ETL platform usually provides an operation interface to display the location of the data pipeline crash and provides steps to restart them. When the data source continues to provide new data and the data structure of the target data storage does not change frequently, the ETL platform is usually used. These platforms are designed for developers to write ETL, so they are most effective for data flow operations that mix proprietary, commercial, and open data storage.
4. Data highway for SaaS platform
Data highway tools such as Scribe, Snaplogic, and Stitch provide a simple web interface that can connect to common data sources, select areas of interest, perform basic transformations, and push data to commonly used destinations.
Another form of data highway helps to consolidate data closer to real time. It operates through triggers, so when the data in the source system changes, it can be operated and pushed to the auxiliary system. IFTTT, Workato and Zapier are examples of such tools. These tools are particularly useful for using "if this" logic when transferring individual records from one SaaS platform to another. When evaluating them, consider the number of platforms they integrate, the functionality and simplicity of the processing logic and price, and any factors specific to your needs.
5. Data preparation tool for users and data scientists
Data preparation tools are usually designed around spreadsheet-like user interfaces to allow users to visualize data profiles and mix data sources. But unlike traditional spreadsheets, these tools capture the data processing steps performed by users and enable visualization and editing operations. Most of these tools can use the scripts they capture to automate the data flow of data feeds with ongoing operational needs.
There are independent data preparation tools, such as Alteryx, Paxata and Trifacta. In addition, traditional ETL vendors such as IBM and Talend have developed data preparation tools for business users and data scientists.
6. API and data integration solutions for application development
If your goal is to develop web or mobile applications that need to connect to multiple data sources and APIs, there are API and application development tools that can simplify these integrations. Rather than integrating data into a central repository, these tools provide various options to support faster application development when using multiple APIs and data sources.
Application integration has several different platform types and tool providers such as Dell Boomi, Jitterbit and Mulesoft platforms designed to simplify API and data access and act as a data bus to centralize interaction. Low-code and mobile development platforms like Built.io, OutSystems and Pow Wow Mobile can be integrated and provide a development and development environment to quickly build and run applications.
7. Big data enterprise platform and data integration function
If you are developing functions on top of Hadoop or other big data platforms, you can choose to integrate data into these data stores:
·You can develop scripts or use ETL tools that support big data platforms as endpoints.
·You can choose an end-to-end data management platform with ETL, data governance, data quality, data preparation and master data functions.
8. AI-driven data integration platform
In the process of cross-scripting, ETL, data preparation, application integration services and big data platforms, it provides developers, data scientists, data administrators and analysts with a lot of actual manual data integration work. Suppliers know this, and some next-generation data integration tools and features will include artificial intelligence (AI) functions to help automate repetitive tasks or identify difficult-to-find data patterns. For example, Informatica is marketing Claire, the "smart data platform", while Snaplogic is marketing Iris, which "promotes self-driven integration."
9. Find the right combination of data integration tools
Considering the type of platform, the number of vendors competing in each space, and the analyst terminology used for classification options, the list of data integration options can be daunting. So, how can you decide the right combination of tools for current and future data integration needs?
The simple answer is that it requires some discipline. First inventory the tools that have been used, compile a catalog of use cases that have been successfully applied, and successfully use these tools to capture people. Provide them with other example use cases that are difficult to implement solutions, so it may be helpful when looking for other tools.
