Alternatives to SSIS(SQL Server Integration Services) – How To Migrate Away From SSIS
SQL Server Integration Services (SSIS) comes with a lot of functionality useful for extracting, transforming, and loading data. It can also play important roles in application development and other projects.
But SSIS is far from the only platform that can provide these services. You might seek alternatives to SSIS because you want a more agile platform that can adjust to your IT ecosystem’s evolving needs. Then again, you might simply want to find a tool that costs less than SSIS.
Regardless of your reason, you have plenty of options to consider.
Criteria for Selecting an SSIS Alternative
If you want to explore alternatives to SSIS, you need to think about how the following factors will affect your business:
Scalability and performance
The scalability of SSIS can increase significantly when you use AWS’s Scale Out to handle package distributions. Before you seek alternatives to SSIS, make sure you try it in conjunction with Scale Out.
Ease of use and learning curve
SSIS has a fairly steep learning curve, which largely depends on your current experience level. If you already know how to move and transform data with Python or R, you might want to seek alternatives to SSIS that rely on your preferred coding language.
Connectivity and source compatibility
SSIS makes it very easy to connect to multiple types of external data sources. In turn, the solution you select must either allow you to write custom connectors or provide a similar list
Common Use Cases
SSIS is mainly used for the migration of data, a fundamental part of today’s business function. Some of its most common uses include the following.
Data Loading
Like many businesses, you likely need to get data loaded into a data warehouse. SSIS makes that easy by supporting multiple data destinations including ODBC, OLE DB, Excel, CSV, flat file, XML, ADO.NET, etc.
Data Cleansing
Your company receives data from many sources, both internal and external. To protect the integrity of your data, you need to standardize and clean the data before loading it into your systems. Otherwise, you risk security breaches and malfunctions due to incompatible formats. Since different sources employ countless formats and standards, you need SSIS for quality data transformation before loading.
Data Archiving
Eliminating older data is risky. Your company needs to preserve it without overloading your system. As a result, your business probably archives data you do not need for your current operations. SSIS can transform your archived information by “homogenizing” the data so that it can be split and merged, making it easy to store and access. Your data can be easily retrieved when you need it.
Before diving into alternatives for SSIS, if your team is looking for an expert team to help you migrate away from SSIS to solutions like Airflow, Azure Data Factory or other alternatives, then reach out today for a free consult!
Open Source SSIS Alternatives
Open-source alternatives to SSIS allow you to modify their code to match your unique needs. Experienced data engineers often prefer that approach. If you don’t have a lot of experience, though, you might find open-source tools intimidating.
Airflow
Airflow started as a workflow management platform at Airbnb. Created by Airbnb in 2014, the open-source project moved to Apache two years later and received a full public release.
Airflow users must create tasks and dependencies with Python. Airflow can then turn the code into a graphical interface that visualizes complex processes.
Google Cloud Platform (GCP), Astronomer, and Amazon Web Services (AWS) have released managed versions of Apache Airflow.
Airflow’s benefits include connecting to diverse data sources and automatically pulling information from those sources on a predefined schedule (typically hourly or daily). Its visual approach to managing data pipelines and management processes helps people reach their goals. Essentially, it’s much easier to understand a complex business practice when presented graphically than as lines of code.
Mage.ai
Mage.ai is an open-source data pipeline platform that positions itself as an alternative to Apache Airflow. Mage connects to most databases and data warehouses. If you know Python or SQL, you can build connections that pull data from more diverse sources.
Mage.ai has a built-in visualizer that lets users view graphical representations of information. Common data visualizing options come with Mage.ai, but you can create custom graphical representation options and save them to the library for future use.
Many Mage.ai users enjoy the flexibility they get from building their own connections, visualization styles, and data transformation processes. The platform comes with some templates you can use in a no-code approach, but you’ll get more flexibility by writing your own Python or SQL code.
Mage also stands out as a great option because development code works just as well in production. Assuming the code works on the development side, it should work once introduced to other users.
Talend Open Studio – Sort Of…
Talend Open Studio is an open-source data integration tool owned by Qlik. The software offers diverse features that include ETL pipelines and data visualization. Users can connect it to various data sources and store the information locally.
Qlik ended support for Talend Open Studio after January 31, 2024. Although the open-source version will not receive updates, Qlik still updates and hosts the proprietary version.
Talend Open Studio has several advantages that could benefit users. It has a straightforward user interface that works well for data professionals and non-technical users. It can collect data from diverse sources, including applications, databases, and file transfer protocol (FTP).
As an open-source tool, organizations could build service-oriented architectures (SOAs) that fit their unique IT ecosystems.
Apache NiFi
Apache NiFi is a low-latency, high-throughput cloud platform that works within any internet browser, so you don’t need to download software. It’s mostly used to manage workflows and business processes. NiFi uses a graphical interface that helps everyone, including data professionals and less technical users, understand workflows, business processes, and which tasks have been completed.
Some of the most helpful features that come with Apache NiFi include a drag-and-drop approach to making pipelines, automatic data transfers, and stream processing that works in near-real-time.
Apache NiFi has a large library of data source integrations for Google Data Source, Amazon Simple Storage Service (S3), and HTTP.
Commercial SSIS Alternatives
If you want alternatives to SSIS that don’t require coding, consider commercial options that will do the heavy lifting.
Informatica PowerCenter
PowerCenter has data source connection, data transformation, data modeling, and data governance features that make it one of the most appealing alternatives to SSIS. More recently, Informatica started encouraging PowerCenter users to adopt its Intelligent Data Management Cloud tool (IDMC), which moves data to the cloud and uses artificial intelligence to improve efficiency and find more streamlined business processes.
Informatica PowerCenter pros and cons
PowerCenter users typically like the tool’s user-friendly interface, which they find intuitive and straightforward. Although it has a slight learning curve, it doesn’t take long for new users to learn how to create and benefit from ETL mappings and workflows.
Reusable pipelines also make PowerCenter an attractive option. Instead of building pipelines from scratch, users can copy or adjust existing work to get excellent results without investing much time.
Of course, there are some limitations. If you typically code in Python, R, or Java, your skills won’t help you customize PowerCenter data connections and pipelines.
PowerCenter also struggles to document processes, which can create problems when working with long workflows and business processes. It could also create issues when sharing work with colleagues and superiors who will want documentation supporting why you made certain decisions.
IBM DataStage
IBM DataStage provides ETL and ELT features to help organizations work with diverse data types. If you have information coming from multiple sources, you likely need to transform data types before you combine them in a database or send them to business intelligence (BI) software.
IBM offers a basic version of DataStage for users who want to keep their data on-premises. If you move your data and other tools to the cloud, you can use DataStage with other products that come with IBM Cloud Pak.
IBM DataStage pros and cons
IBM DataStage scores well for its large library of data connections, diverse data transformation options, and intuitive data modeling. It’s also an industry leader in data governance.
However, some users struggle to manage big data and take advantage of the most advanced data transformation options. Users who run into these and other issues often complain that IBM doesn’t offer strong customer services for DataStage.
Alteryx
Alteryx claims that it can do it all:
- Collect data from diverse sources.
- Transform any data type.
- Analyze data from a simple, drag-and-drop interface.
As long as you don’t need a custom approach, you can rely on low-code and no-code features to reach your goals. Personally, I find Alteryx
Alteryx even uses natural language processing and optical character recognition to automate jobs.
Alteryx pros and cons
Most users find that Alteryx is an impressive option for aggregating, validating, and analyzing data. The low-code/no-code options work well for most people. They also emphasize the helpfulness of Alteryx’s adaptability. It can connect to practically any data source and generate reports in almost any format.
Regarding negative aspects, users complain that it isn’t as customizable as tools that use R or Python. Similarly, creating custom integrations can take more time than expected.
Cloud-Based SSIS Alternatives
Cloud-based alternatives to SSIS make it possible for your team members to access data from any location with an internet connection. That’s potentially a huge boon considering today’s remote and hybrid work trends.
Azure Data Factory
Azure Data Factory primarily serves as a cloud-based data integration service. You can also use it in hybrid environments that combine on-premises servers with cloud technologies.
Azure Data Factory pros and cons
Data Factory comes with a library of 90+ data integrations that will meet the needs of most users. The platform’s architecture lends it to continuous integration and continuous development (CI/CD). Using that feature of the architecture requires some experience with Git, which might confuse non-technical users until they’ve been properly trained.
On the downside, Azure Data Factory can produce cryptic error messages that are hard to understand, which makes it challenging to address issues. Other common complaints include cluttered pipelines, granular errors, and poor collaboration between the cloud and on-premises systems.
AWS Glue
AWS Glue provides serverless data integration services optimized for machine learning, analytics, and app development. It has a library of 70+ data connectors and integrates easily with other AWS services.
AWS Glue pros and cons
Most Glue users praise the tool’s rapid scaling, customizable workflows, and architectural integration with the broad AWS ecosystem.
The most commonly cited problem is that Glue only works with AWS. You can’t use it outside of Amazon’s ecosystem. Some users also complain that Amazon throttles the service, making it difficult to reach time-sensitive deadlines.
Google Cloud Dataflow
Google Cloud Dataflow excels at streaming and analyzing data to detect fraud and discover insights in real time. The platform includes a built-in business intelligence (BI) dashboard that connects to most data sources and data movement that consumes and replicates information across subsystems.
Google Cloud Dataflow pros and cons
Notable features of Cloud Dataflow include its rapid scalability, incredible fault tolerance, and high availability, which aren’t things you always find in serverless platforms.
On the other hand, Google Cloud Database doesn’t collaborate well with on-premises systems. Costs can also seem a little opaque, especially compared to other Google services.
Key Considerations for Migration
If you decide to switch to a different ETL tool, you must undertake a migration project that moves your data to a new location and updates your current connectors.
Some platforms make migration easier than others. Before you adopt a new tool, assess your current ETL processes and integration needs to ensure the alternative can fill those roles.
You also need to evaluate the new tool’s learning curve and community support. How long will it take your employees to use the new platform and reach their full potential?
If you want alternatives to SSIS that save you money, look at the total cost of ownership. Make sure you include migration costs. They can add up quickly.
Finally, ask companies whether they will let you test and validate strategies before you fully embrace new ETL tools. You must know that the tool works for you before you migrate. Data migration takes enough effort that you need certainty you can proceed once you start using the new ETL tool.
Are You Ready for Alternatives to SSIS?
SSIS is a good data migration tool, but it isn’t the right option for everyone. If you already use SSIS, don’t jump ship until you’ve done plenty of research. You need to speak with each company’s representatives, review demos, and “test drive” platforms.
Your chosen tools will partially define your business success, so you can’t take this decision lightly. Ask department heads to weigh in so you have multiple perspectives. When you find a platform that fits your criteria, you’ll know it’s time to move on.