About the Services
Azure Data Factory
Azure Data Factory (ADF) is a cloud-based data integration tool from Microsoft. It launched in 2015. And now, it’s popular among enterprises and individuals alike.
ADF offers a wide range of data integration scenarios. This includes ETL, ELT, reverse ETL, data ingestion, replication, and more. You can use over 90 built-in connectors. This covers on-premises and cloud-based data. You can use Azure Data Factory to move data from sources such as SQL Server, Oracle, Salesforce, and SAP. And into destinations such as Azure SQL Database, Azure Blob Storage, and Amazon S3.
Microsoft takes customer data privacy and security very seriously. Microsoft designed Azure Data Factory with several security features. This includes Azure Active Directory integration, role-based access control, data encryption, and more. Moreover, the tool complies with security and privacy standards. This includes GDPR, SOC 1/2, ISO 27001, and HIPAA. This means that you can trust Azure Data Factory to handle your data in a secure and compliant manner.
You can also use ADF by importing SQL Server Integration Services (SSIS) packages.
Apache Airflow is a free, open-source platform for developing batch-oriented workflows. It launched in 2014 and has big-name companies like Airbnb, Lyft, and Etsy in its portfolio. It has a growing community of over 2,000 contributors and many users worldwide.
Apache Airflow uses a workflow management system. This system handles automating and monitoring data integration processes. It can handle simple data transfers and complex machine-learning workflows. The user interface is easy to use, so you can focus on your work instead of minding a confusing interface.
But having that simple interface has a caveat. Open-source platforms need technical expertise, and Airflow is no different. You can start with several options to install. If you don’t want to hire experts to do this for you, you need to know about Python and some required libraries and even about Docker Containers for a seamless cloud deployment.
Apache Airflow also doesn’t have a record of privacy and security certifications, but it has security features like role-based access control and encryption. So, your data is safe from prying eyes. It also provides logging and auditing capabilities. So, this will help identify and investigate any security or privacy incidents. Companies with stringent security and privacy requirements trust Apache Airflow. But again, either you need some more technical expertise, or pay for someone who knows how to secure Airflow in your environment.
Skyvia is a no-code cloud data integration platform for many data integration scenarios. It’s an all-rounder tool for ETL, ELT, Reverse ETL, data migration, one-way and bi-directional data sync, workflow automation, and more. Devart launched this fantastic product in 2014 for cloud data integration and backup.
Skyvia offers more than 160 ready-made data connectors. These are available for thousands of free users, including 2000+ paid customers. Big names like Hyundai and General Electric trust Skyvia to process their data. Its easy-to-use, drag-and-drop interface suits both IT professionals and business users. And don’t take our word for it. Listen to G2 reviewers about how easy it is to start and work with it. Data integration experts who used other tools can adapt with little to no help from support.
Skyvia has flexible pricing plans perfect for small startups and large enterprises. So, it makes it applicable to businesses of all sizes. Also, Skyvia’s freemium model allows users to start using it now and then decide if they need to upgrade later.
The safety of your data is also our prime concern. So, we hosted it in Microsoft Azure cloud, providing the best data security and privacy. It complies with a wide set of security standards, including SOC 2, ISO 27001, and many others.
|ETL, ELT, Reverse ETL, streaming.
|ETL, ELT, and reverse ETL
|Data ingestion, ELT, ETL, reverse ETL, data sync, workflow automation.
|Low-code, no-code solutions.
Coding in various languages for complex scenarios.
|Python coding skills.
|No-code and easy to use wizard.
|Supported data sources.
|Supported data sources.
|Supported data sources, including databases, data warehouses, cloud apps and flat files.
|Full and incremental load.
|Full or incremental load.
|Full table and incremental via change data capture.
|Ability for customers to add new data sources
|Provides SDK for creating custom connectors.
|Yes, by coding.
|Yes, by request or using REST API connector.
|G2 customer satisfaction
|4.6 out of 5
|4.3 out of 5
|4.7 out of 5
|Peer Insights satisfaction
|Azure Portal, CLI, and PowerShell.
Azure Functions for transformations.
Use external services like HDInsight.
|REST connector for data sources that have REST API.
|Advanced ETL capabilities
|Importing SSIS packages.
Calling External processes from the pipeline.
Use Hadoop Streaming.
|Integration with other integration tools like Kafka, dbt, Airbyte, and more.
|Visual ETL data pipeline designer with data orchestration capabilities.
|Compliance and security certifications
|SOC 1/2/3, ISO 27001 / 27017 / 27018, HIPAA, GDPR, CCPA, FedRAMP, Dod SRG, ITAR
|No official list of certifications.
|HIPAA, GDPR, PCI DSS.
ISO 27001 and SOC 2 (by Azure).
|Self-service through Azure Portal or contacting Microsoft sales.
|Download and install.
|Self-service or sales.
|Pay-as-you-go or consumption basis.
No minimum commitment or contract term.
|Monthly or annual contracts.
|Always Free for 5 low frequency jobs.
Included in Azure Free Trial with $200 credit for 30 days.
|Cloud-hosted with volume-based pricing.
Self-managed with a customized package.
With a free tier and 14-day trial.
|Volume-based and feature-based pricing. Freemium model allows to start with a free plan.
Azure Data Factory
Azure Data Factory has 90+ built-in connectors for integrating data from various sources. Microsoft regularly adds new connectors and update existing ones. The connectors can integrate databases, cloud platforms, big data, and SaaS applications. Popular connectors include Azure Blob Storage, Amazon S3, and Salesforce.
It also supports generic REST, OData, HTTP, and ODBC connectors. You can create custom connectors using .NET, Java, or Python. ADF provides a software development kit (SDK) for this purpose. And when you’re done with your new connector, you can share and reuse it to others in your organization. Or with other users outside your company.
Apache Airflow provides 80+ built-in data connectors and provider packages. These connectors will work with various types of data sources. And this includes databases, cloud platforms, and messaging systems. It can also deal with popular APIs, data warehouses, and lakes. Some of the most popular connectors include MySQL and PostgreSQL.
But, if you need one that is not available, you can create your own using Airflow’s API. Additionally, the Airflow community never stops developing and sharing new connectors. So, if your data source is so unique and recent, look for a connector in the community first. But if nobody made one for it yet, prepare to roll up your sleeves and code.
Skyvia offers more than 160 connectors, and more to come very soon. It supports connectors for CRMs, accounting, email marketing, e-commerce, human resources, marketing automation, payment processing, product management, all major databases and DWH, flat files, and more. It’s also not a problem whether your data is on-premise or in the cloud.
You can access your on-premise data with peace of mind using the Skyvia Agent. It allows you to connect to databases like SQL Server, MySQL, and more using an encrypted connection. You need to download the Skyvia Agent and install it. Then, download a secured key file and place it in the same folder as the Agent. The Agent is like an unbreakable metal door, and you use the key file to open that door to your on-premise data. You can also set it up so that Skyvia can access only the resources you specify and nothing else.
Customers can also leave a request for a new data connector. And Skyvia will prioritize building it without additional payment.
Azure Data Factory
Azure Data Factory provides flexible and powerful options for data transformation. ADF supports various transformations, including filtering, aggregating, joining, sorting, and more.
You can use the graphical user interface to add transformations or code to create advanced data transformations. ADF allows coding in several programming languages, including SQL, .NET, Python, and others.
It also supports external activities for executing your transformations on compute services. This includes HDInsight Hadoop, Spark, Data Lake Analytics, and Machine Learning. This gives you the flexibility to use different approaches in data transformation.
Apache Airflow provides a flexible way to handle data transformations. Airflow supports a variety of data transformation tasks. This includes data cleansing, aggregation, filtering, and enrichment. You can perform transformations using code-based approaches. So, if you prefer to click than code to perform transformations, this is not for you.
Airflow has a web, graphical interface for data transformations, but coding is always required. So, it supports programming languages like Python and SQL.
Skyvia is a full-featured ETL service that allows powerful data transformations. It is a no-code solution allowing data splitting, conversion, lookups, and many more.
You can use the Skyvia Data Flow and Control Flow for advanced data pipelines. Transformations for these advanced pipelines are flexible. It supports extending your data with new columns, conditional flows, and summarized values. And all these you can do with parameters, variables, and more for flexibility without code.
Moreover, Skyvia has an Expression Builder to build formulas with many functions. With this, you can convert or extract parts of the data or form new values to suit your needs. And if you love coding in SQL, Skyvia can further extend your transformation needs. It supports multiple joins, groupings, CASE expressions, and more in SELECT queries. And you can also use DML commands like INSERT, UPDATE, and DELETE.
Azure Data Factory
Azure Data Factory offers several levels of customer support. It includes free and paid options. The free support includes online documentation, community support, and email support.
Paid support options include Standard, Professional Direct, and Premier. Each has various levels of 24/7 support and faster response times. The Standard and Professional Direct support levels also come with an SLA. It guarantees a certain level of uptime and issue resolution time. Customers can reach the support team through various channels. This includes email, phone, and online chat. For premium support, customers can avail themselves of dedicated support teams and other benefits.
Azure SLA guarantees 99.9% uptime for paid Azure services.
Airflow has an active and helpful community, and users can access various support channels depending on their needs. Their website provides documentation and guides for all levels of users. They also have a mailing list where users can ask for help and get support from the community.
For those who need more help, premium support is available through third-party vendors. These vendors provide support levels, including response time guarantees and service-level agreements (SLAs). They also offer training and consulting services.
Users can reach out to these vendors through their websites or by contacting them directly to benefit from their premium support services. With these options, users can choose the level of support that fits their needs and budget.
Skyvia offers free email, chat (on the website or in-app), and forum support for all customers. It also provides extensive documentation with lots of tutorials and user guides.
For paid customers, there's also a phone support option and additional support options for Enterprise customers.
Azure Data Factory
Azure Data Factory provides a flexible pay-as-you-go pricing model. It charges based on pipeline orchestration, data movement, and data volume. Pricing can vary depending on region and usage patterns.
New customers get $200 free credit for 30 days to use any Azure service, including Data Factory. Pay-as-you-go is your next option once the trial ends or the free credits becomes zero. But ADF is always free for 5 low-frequency activities.
Some services may need resources not covered in Always Free services like storage. So, it’s crucial to check pricing and usage limits.
Apache Airflow is an open-source platform, which means it’s free to use. There are no hidden charges or fees to access and use Airflow’s core features.
Moreover, Apache Airflow doesn’t need any upfront payment, subscription, or contract. Users can download, install, and run the software. They can do that on their own hardware or cloud infrastructure. They have full control over their deployment, scaling, and maintenance. This makes it an attractive option for those who want to avoid vendor lock-in.
There are also no user, workflow, or data source limitations in using Airflow. You can experiment, innovate, and collaborate on your pipelines without worries.
But this flexibility needs a lot of skilled work hours. So, if you need a data integration tool that is ready to use from day 1, this may not be an attractive option.
Skyvia Data Integration is a freemium tool with an option to request a 14-day trial. So, price is not a barrier to entry.
And when you’re ready, paid plans start from $19 per month. Pricing tiers depend on a few factors. It includes the number of loaded records, scheduling frequency, and advanced ETL features. There are no sale commitments. And customers can upgrade or downgrade at any time. Check out a detailed comparison here.
If you doubt the price is worth it, check out review sites like G2. Aside from ease of use, reasonable pricing is one of the things Skyvia customers like. So, you can be sure the features you get are worth every penny.