Azure Data Analytics Partner
Trusted by Innovative Companies
Azure Data Analytics Services
Data and Analytics Strategy
Plan your Azure data analytics architecture with confidence and define the path to become more informed.
Data Discovery and Orchestration
Eliminate silos and set up a unified repository with analysis-ready input and standardized processing.
Migration and Integration
We help you securely move to the cloud and consolidate your workflows to achieve maximum consistency.
Visualization and Reporting
Let your data tell stories and emphasize the message through dynamic and business-led visuals.
Business Intelligence
Our BI developers help you stay ahead of the competition and grow safely into the future.
Data Lake Analytics in Azure
Make Big data look simple and transform complex information into actionable guidelines.
Our Big Data Analytics Solutions on Microsoft Azure
- Synapse Analytics
- Predictive insights
- Smart warehouse workload management
- Databricks
- All-in data analytics on Azure
- High-performance Azure data warehouse analytics
- Microsoft Purview
- Easy warehouse governance
- Seamless discovery and insights
- Azure Data Factory Analytics
- Managed integration service
- Codeless transformation and analysis
- Azure HDInsight
- Azure cloud data analytics
- Freedom of open source in cloud
- Azure Data Stream Analytics
- Serverless streaming pipeline
- Hybrid analysis at edge
- Azure Machine Learning
- Automated model training
- MLOps deployment and management
- Azure Analysis Services
- Managed analysis infrastructure
- Self-service data discovery
- Data Lake Storage
- Azure data lake analytics and Power BI
- On-demand scalability
- Azure Data Explorer
- Managed analysis of streaming input
- Data exploration
Gain from Azure Data Analytics
Industries
Manufacturing
Why InData Labs?
Team of Experts
A team of 80+ experts will take care of your cloud project and build a custom cloud architecture fully adjusted to your needs.
Rich Experience
We have successfully delivered over 150+ cloud, analysis, and BI projects to global companies across domains.
Mature Cloud Partner
Our cloud expertise and technology excellence was recognized by top industry rankings.
Certification
Let Our Clients Do the Talking
Customer Success
Frequently Asked Questions
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Azure data lake analytics architecture is a cloud-based processing service that allows you to build and execute large-scale analysis programs. The service is designed to handle both structured and unstructured input, as well as input from a variety of sources. With Azure data and analytics, you can process input in real time or batch mode, and you can run your programs on-demand or schedule them to run at specific times.
However, it is important to note that the service is not a substitute for a traditional warehouse. If you need to store and query large amounts of input, you will still need a warehouse. Azure data lake analytics service can complement your warehouse by providing a way to process and analyze input in a more efficient and cost-effective manner.
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Data Lake Analytics on Microsoft Azure is a platform that enables you to make sense of the large sets stored in the Lake and supports Big data engineering. It offers a rich tool box that allows you to process and analyze input of any size, shape, or type.
With this tool, you can easily get insights from your assets without having to worry about the burden of infrastructure management.
Some of the key capabilities of data lake analytics Azure include
- Scalability: the architecture can scale to meet the demands of your business. It can handle information of any size, shape, or type, and you can easily add or remove capacity as needed.
- Security: the tool is designed to be secure and compliant with industry standards. It amplifies your security through single sign-on, multi-factor authentication, granular access control, and other measures.
- Management: the tool offers simplified and guided asset management supported by managed infrastructure.
Azure data lake analytics pricing varies and is based on the Pay-as-You-Go model and monthly commitment packages.
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It is a cloud-based processing and analysis service from Microsoft. It is designed to help businesses get insights from their assets quickly and easily. ADFA is built on top of the Azure Data Factory platform, which provides a scalable and reliable cloud infrastructure for processing and analysis.
ADFA provides a variety of built-in processing and analysis tools, as well as a flexible workflow system that allows businesses to customize their processing and analysis pipelines. ADFA also offers a variety of integrations with other Azure services, such as Databricks, HDInsight, and Machine Learning for AI companies.
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The Azure ecosystem is one of the main players in the cloud market. Around 70 percent of organizations worldwide use Microsoft Azure for their cloud services and digital asset management. At the same time, around 95 percent Of Fortune 500 companies are also among the biggest adopters of Azure.
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Both Synapse and Azure Lake are two cloud services that allow you to analyze and find patterns in your assets. Both services allow you to load input from multiple sources, process it, and then query it to get insights.
So what’s the difference between the two? Azure Synapse is designed for data warehouse services, while Azure Data Lake Analytics is designed for Big data analytics on Azure. Synapse is a managed cloud service that offers fast performance and scalability as well as slashes time to insight. Data Lake Analytics is a serverless cloud service that allows you to analyze data with no on-premise infrastructure.
Your choice should depend on your needs. If you need fast performance and scalability, Synapse is a good choice. If you need a serverless solution that is easy to set up and use, Lake Analytics is a good choice.
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Since Microsoft Azure is a closed-source tool, it has the same pros and cons as any other commercial software. When it comes to software, there are two main types – closed-source and open-source. One benefit is that it can be more secure than open-source software, as the code is not publicly available and can only be accessed by those with permission.
Closed-source software is also more reliable, as it is typically developed by professional developers and undergoes more testing than open-source software. Finally, closed-source software is more user-friendly, as it is typically developed with the user in mind and includes documentation and support. Also, Microsoft Azure has a fully managed infrastructure and gets regular updates.
As for the cons, SaaS cloud services are available on a paid basis.
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