Today's data often comes from smileys, file systems,
data lakes or repositories. In order to meet various business needs, we must integrate data with other data source systems of record to support analysis, customer-oriented applications or internal workflows. And this brings a new problem-how do we choose the right data integration tool to summarize all kinds of data? Today's article will discuss this.
1. Data technology and function market is huge
The question is: What tools and practices are used to integrate data sources? What platforms are used to
automate data manipulation? Which tools are being commissioned for data scientists and data analysts to work harder when using new data sources? In development across multiple data sources and When using APIs for trading applications, effective development and development tools can achieve faster application development?
Since many organizations have different types, amounts, and speeds of data, and have different business requirements over time, there may be different methods and tools for integrating data. It is easy to stick to these and extend them to new use cases. Although anyone who uses data tools may be more familiar with one method than others, for organizations with multiple business and user needs, applying a one-size-fits-all data integration method may not be the best choice.
In addition, as more and more organizations invest in data solutions, there is a healthy market for big data solutions. The result is that there are now many new platforms and tools to support data integration and processing.
With so many tools, organizations that want to use data processing as a core function should consider various tool types and apply these tool types according to business and technical needs. Technicians working with or in charge of data technology should be familiar with the types of tools available. Here, I investigated seven main types of tools:
·Programming and script
data integration
·Traditional extraction, transformation and loading (ETL) tools
·Data Highway SaaS Platform
·Data preparation tools for users and data scientists
·API and data integration for application development
·Big data enterprise platform with data integration function
·AI injection data integration platform
2. Data integration programming and scripting
For anyone with basic programming skills, the most common way to move data from a source file to a target file is to develop a short script. This can be done in a database with stored procedures, as a script that runs on a scheduled job, or it can be a small piece of data processing code deployed to a serverless architecture.
These scripts usually run in one of several modes. They can run on a pre-defined schedule or as services triggered by events, or respond when defined conditions are met. They can obtain data from multiple sources, join, filter, clean, verify, and transform the data before sending it to the target data source.
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 transmit 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. The programmer should also perform important calculation steps to record for easy debugging.
Writing scripts to support these operational requirements is not trivial. It requires developers to predict possible problems with data integration and corresponding programs. In addition, developing custom scripts may not be cost-effective when using many experimental data sources. Finally, data integration scripts are often difficult to complete knowledge transfer knowledge and difficult to maintain across multiple developers.
For these reasons, organizations with greater data integration needs often go beyond programming and scripting data integration.
3. Traditional extraction, conversion and loading (ETL) tools
Since the 1970s, extraction, transformation, and loading (ETL) technology has emerged, and platforms such as IBM, Informatica, Microsoft, Oracle, and Talend have matured in terms of functionality, performance, and stability. These platforms provide visual programming tools that allow developers to decompose and automate the steps of extracting data from the source, performing transformations, and pushing the data to the target repository. Because they are visual and break the data stream into atomic steps, pipelines are easier to manage and enhance than scripts that are difficult to decode. In addition, ETL platforms usually provide an operation interface to show the location of data pipeline crashes and provide steps to restart them.
Over the years, many functions have been added to the ETL platform. Most people can process data from databases, flat files, and web services, whether they are locally, in the cloud, or in SaaS data storage. They support various data formats, including relational data, semi-structured formats such as XML and JSON, as well as unstructured data and documents. Many tools use Spark or other parallel processing engines to parallelize jobs. Enterprise-level ETL platforms usually include data quality functions, so data can be verified through rules or patterns, and exceptions can be sent to data administrators for resolution.
A common ETL example is when an organization loads a new file of sales prospects into the CRM. Before loading, these data sources usually need to clean up physical and email addresses, which can be done by using rules and standard data sources for conversion. Then match the cleaned records with the records that already exist in the CRM, so that the existing records are enhanced, and at the same time add data that was not before and add new records. If it is difficult for ETL to determine whether a row is a match or a new record, it can be marked as an exception for review.
When the data source continues to provide new data and the data structure of the target data store 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
But, is there a more effective way to extract data from common data sources? Perhaps the main data target is to extract accounts or customer contacts from Salesforce, Microsoft Dynamics, or other common CRM programs. Or, marketers want to extract web analysis data from tools such as Google Analytics, or try to push customer data into marketing tools (such as Mailchimp). How should you prevent the SaaS platform from becoming a data island in the cloud and easily achieve bidirectional data flow?
If you already have an ETL platform, please check whether the supplier provides a standard connector for the general SaaS platform, or there is a market that can be purchased from a development partner.
If you have not invested in the ETL platform, and your data integration needs are mainly to connect to a common platform, then you may need an easy-to-use tool to build a simple data highway.
Data highway tools such as Scribe, Snaplogic, and Stitch provide simple web interfaces that can connect to common data sources, select areas of interest, perform basic conversions, and push data to common destinations.
Another form of the data highway helps to integrate 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 so" logic when transferring individual records from one SaaS platform to another. When evaluating them, please consider the number of platforms they integrate, the functionality and simplicity of the processing logic, and the price, as well as any factors specific to your needs.
5. 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 determine the right tool combination for current and future data integration needs?
The simple answer is that it requires some discipline. First, count the tools that have been used, compile a catalog of successfully applied use cases, 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.
Understand the feelings of data integration subject matter experts. Maybe there are data integration scripts that need to be maintained continuously, the finance team is frustrated with repetitive work, or the development using ETL solutions is too slow for the needs of the marketing team. Perhaps a data scientist spends a lot of time using a programming language to entangle data and create a huge code base. Perhaps many data integration requirements are related to a few standard platforms, and standardized integration methods will bring operational benefits.
With a checklist, a team of data integration experts can review implementation options when requesting new or enhanced data integration. If the new request is like a request that has been implemented and is working, the team should be confident to apply it again. If not, it can choose to try existing tools for implementation, or consider using new tools for proof-of-concept, if this is a highly different data integration job.
When there are new business requirements and a constantly changing technological environment, it is best practice to integrate use cases and review new use case specifications.
