SSIS vs. ETL Best SSIS Alternatives of 2024

 Editor’s note: This article has been updated for accuracy and comprehensiveness in October 2023.

Table of contents

  1. What is ETL?
  2. What is SSIS?
  3. Best SSIS Alternatives
  4. SSIS vs. ETL: Which Is the Future of Data Transformation in 2024?
  5. Conclusion

What is ETL?

The ETL is a generalized term for a process that does Extract-Transform-Load. In other words, we collect data from different sources and formats, transform it into appropriate forms for analysis, and load it into the target DB or DWH to store. These three steps are essential in the modern data-driven world, allowing businesses to improve data quality, scalability, and analysis and optimize time and costs.

Let’s see what kind of tasks the ETL tools may do:

  1. Provide a uniform interface for data sources and destinations.
  2. Apply transformation logic on the source data based on target data source requirements.
  3. Handle multiple data sources and formats in a single workflow.
  4. Require error handling logic so that they can eliminate bad data without any user intervention.
  5. Perform data transformation such as sorting, filtering, grouping aggregation, cleaning data, and removing duplicates.
ETL

Note: You can find more information about What is ETL? with Skyvia’s beginner-friendly guide.

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What is SSIS?

SQL Server

The SQL Server Integration Services (SSIS) is an ETL tool that can embed complex business logic load data from various data sources into SQL Server, Sharepoint List destination, flat files, etc. It’s a part of the Microsoft SQL Server database software family. The SSIS contains workflows for performing the task in serial or parallel mode. It can create constraints for different execution paths based on the task’s success, failure, or completion.

It’s also a popular tool among database administrators for designing maintenance tasks such as backups, restore, indexing, and consistency checking.

History of SSIS

It has a long history of improvements over time in terms of features, reliability, speed, and stability. Many of us can testify to this because we managed to use the Data Transformation Services (DTS) in SQL Server 7 before it became SSIS.

  1. SQL Server 7 and 2000: Microsoft introduced Data Transformation Services (DTS) in SQL Server version 7 and 2000.
  2. SQL Server 2005: SQL Server 2005 got Integration Services (SSIS), a revamp of earlier DTS with new looks and enhanced features.
  3. SQL Server 2008: SSIS has more data sources and performance improvements for handling significant amounts of data with minimum resources in this version.
  4. SQL Server 2012: This is a milestone for SSIS as it includes many concepts, such as the project deployment model. The project deployment model consists of a project file in the ISPAC extension, a self-contained deployment unit, and information about ETL packages with parameters stored in the project.
  5. SQL Server 2014: Includes new data transformation tasks and source and destination connections using an additional SQL Server feature pack.
  6. SQL Server 2016: The SQL Server 2016 version includes an Incremental Package Deployment feature, which allows you to deploy one or multiple packages instead of deploying the whole project. Earlier versions required full package deployment only. Note: Refer to Microsoft docs for detailed information on the new features in SSIS 2016.
  7. SQL Server 2017: This version includes scales-out for distributing SSIS package execution across multiple worker computers, Integration Services on Linux, and connecting to the OData feeds for Microsoft Dynamics AX Online. 
  8. SQL Server 2019: This version has improved file operations, such as performing file operations on Local File System, Azure Blob Storage, and ADLS (Azure Data Lake Storage) Gen2. 

What is the SSIS Package?

The SSIS package is a collection of the necessary components for a specific ETL task. These components are the following:

  1. Source and destination connections
  2. Tasks
  3. Control flow
  4. Data flow
  5. Configurations
  6. Variables and parameters
  7. Event handler

The diagram below depicts the high level of ETL packages in SSIS.

ETL packages in SSIS

Key features of SSIS

  1. Source and destination connections: The SSIS consists of multiple in-built sources and destination connectors. These connectors can connect to flat files (TXT, CSV, XLS), Relational databases (SQL Server, Oracle, MySQL, etc.), OLEDB, ODBC, Azure Connections, WMI, SMO, FTP, Mail servers, etc. You can use connectors such as SQL Server Destination instead of OLE DB to load the data faster.
  2. SSIS transformation tasks: The SSIS has multiple data transformation tasks such as Row transformation, Split and Join transformations, Aggregate transformation, Data Conversion Transformation, Conditional Split Transformation, Cache Transform Transformations, Auditing Transformations, Fuzzy Grouping, and Lookup SSIS Transformations and Custom Transformations with quick configurations and deployment.
  3. Graphical interfaces and workflow: The SSIS is a workflow-oriented tool that defines control and data flow tasks using precedence constraints. It includes a friendly GUI tool using SQL Server Data Tools to define tasks easily. You can also monitor execution and view central logs for all packages stored in SQL Server.
  4. Package deployment and reuse: The SSIS allows a package deployment model for deploying ETLs across multiple servers, migrating and reusing them as required.
  5. Comprehensive documentation and support.

Cons of SSIS

  1. You cannot develop SSIS packages on Linux. But you can deploy them on SQL Server 2019 for Linux and run them from there. However, there will be certain features not available on Linux.
  2. The SSIS requires developer experience and complex coding for applying business logic except for cases when you need simple loading and transformation of CSV and writing rows in SQL Server.
  3. It requires Microsoft SQL Server Management Studio to view logs or SQL Server Data Tools to view execution logic. It isn’t easy to fetch information using the command-line tools.
  4. It requires a complex transformation process for the JSON data.
  5. The SSIS memory consumption can be pretty high when working with multiple packages in parallel.
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Best SSIS Alternatives

There are various SSIS alternatives that you can use for building ETL packages. Among such ETL tools are the following:

  1. Skyvia
  2. Talend ETL
  3. Python
  4. Azure Data Factory
  5. AWS Glue

Read more about these ETL tools below. We tried to highlight the key points of them.

SSIS Alternatives: Skyvia

Skyvia

Skyvia is a cloud ETL and ELT data integration tool compatible with both on-premises and cloud sources. Skyvia supports 180+ connectors, including cloud apps, databases, and data warehouses.

Skyvia offers several advanced data integration tools described below.

  • For performing ETL operations, Skyvia offers an Import tool with advanced mapping settings for data transformations. It preserves all relations between the imported files, tables, or objects; supports INSERT, UPSERT, UPDATE, and DELETE operations; and offers detailed logging, scheduling, and error handling functionality.
  • For performing ELT operations, Skyvia offers a Replication tool. Skyvia Replication helps extract data from a source and load it into the target. Unlike the Import tool, Replication doesn’t support complex transformation or using any custom mapping. However, it’s much simpler to configure. It doesn’t require a database with a prepared schema — it can automatically create database tables for cloud data.
  • For more complex ETL/ELT scenarios, Skyvia offers Data Flow. and Control Flow. It helps to design complex data pipelines involving multiple data sources. It combines data from several sources, loads data to multiple destinations, and performs advanced multistage transformations on the way to destinations. Plus, you have a visual diagram of the process, which is quite convenient.

Advantages of Skyvia

The main advantage of Skyvia is the impressive combo of cool functionality, useability, and honest price

  • Start from anywhere just while registering into the app with your laptop. The solution is no-code, cloud-based, and doesn’t need any additional training to use it.
  • Work comfortably thanks to Skyvia’s simplicity and useability. In contrast with SSIS, the UI is intuitive, and the functionality doesn’t need additional software installation and hardware configuration.
  • Enjoy your precise and up-to-date data with various ETL, ELT, and Reverse ETL integration scenarios.
  • Pay as you go depending on your organization’s needs, or you may use the tool for free if this functionality is enough for right now.
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SSIS Alternatives: Talend

Talend

Talend is an open-source ETL data integration tool compatible with both on-premises and cloud sources. It runs on the Java platform and utilizes Java for advanced scripting components. Talend also uses metadata to store the connection strings for XML, Excel, Web Services, FTP, etc.

In the Talend, ETLs are defined as jobs, while SSIS defines them as ETL packages. The solution offers scheduling, logs, and error services similar to the SSIS package.

Advantages of Talend

  • It has a user-friendly interface with a straightforward design of jobs.
  • Talend has streamlined data warehouse management in comparison with SSIS ETL.
  • It utilizes JDBC connections to database systems and provides better error management.

SSIS Alternatives: Python

Python

Python is an open-source machine language that consists of a considerable number of packages for executing tasks with minimum coding. Python is a code-based language to define your logic and utilize the frameworks for performing ETL tasks compared with the SSIS packages. However, you require coding experience to use it for various data migrations.

Advantages of Python

The top advantages of Python as the SSIS alternative are its flexibility, cost-effectiveness, and rich library ecosystem.

  • Contrary to SSIS, Python doesn’t need a specialized environment and may be easily updated with necessary libs and packages, widening its use-cases range. 
  • Contrary to SSIS requiring an SQL Server license, Python is open-source, so you don’t need to pay for it. 
  • Python provides a wide range of libs like Pandas, NumPy, Dask, Apache Airflow, etc., for data processing. However, SSIS may require additional components and more complicated approaches to reach the same result.

SSIS Alternatives: Azure Data Factory

Azure Data Factory

Azure Data Factory is a browser-based ETL tool with robust scheduling and monitoring features. Like SSIS, ADF supports aggregations, fuzzy lookups, derived columns, and other visually designed data transformations. The ADF data flows, and databricks have spark clusters that transform and process analytics workloads. Azure Data Factory supports event-based, tumbling window triggers and scheduled batch triggers.

Advantages of Azure Data Factory

  • You do not need to manage infrastructure for the Azure Data Factory, and you can start building your ETLs as pay-as-you-go subscriptions.
  • The solution supports over 90+ in-built sources for processing complex data requirements. You can use third-party or customer connectors for processing JSON and REST APIs.
  • You can migrate your SSIS packages in the Azure Data Factory or execute them using integration service runtime.

SSIS Alternatives: AWS Glue

AWS Glue

AWS Glue is Amazon Web Services’ fully managed ETL for big data and data analysis. It’s also a serverless data integration service with capabilities required for data integration from various sources. It offers both visual and code-based interfaces for both beginners and experienced data professionals. AWS Glue has automatic crawling functionality that identifies data formats and suggests schema tables for storing your data.

Advantages of AWS Glue

  • You don’t need to manage servers because of the AWS Glue serverless architecture.
  • It supports event-driven ETLs so your process can automatically run in case of a specific event, like the arrival of new data files.
  • AWS Glue supports unified catalogs that help you find relevant data from multiple data sources in a single pane.

SSIS vs. ETL: Which Is the Future of Data Transformation in 2024?

Of course, SSIS is a good enough ETL tool to solve the appropriate tasks, but the ETL term is more comprehensive and means a wide range of solutions and techniques. The data transformation future isn’t only about extracting, transforming, and loading, as SSIS provides. It’s more about real-time data processing and analytics, flexibility, scalability, cloud capabilities, cost-efficiency, and compatibility with appropriate sources and solutions.

Conclusion

Each ETL tool is different in functionality, data sources, transformations, and visualization. You should compare the products as per your feature requirements and accessibility.

We recommend exploring the Skyvia data integration tool that could fit most users’ requirements.  

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