The client is a large retail company looking to obtain a comprehensive view of their customer behavior. They needed a consolidated data view based on a unified cloud architecture.
The InData Labs team built and delivered a custom cloud platform that pulls information from multiple sources and blends it with other SaaS business applications.
Our client is a food service company looking to move their on-premise workloads into the cloud. Our team was challenged to migrate legacy on-site assets into the cloud with IoT high-load processing.
InData Labs has planned and performed seamless migration to Azure data lake in multiple phases. The result is a high-performing architecture with uninterrupted information flow and real-time sensor processing.
Our client is a chemical manufacturing company looking for a more intelligent way to examine the chemical composition of products and their sales data. The main challenge was complex production data with resource-intensive querying.
Our team has delivered an Azure data analytics platform for real-time sales and product analytics that allows the Client to generate and visualize insights on product data.
Our client needed a robust analysis solution to make sense of large amounts of construction information with instant visualization of detailed KPIs and custom charts.
The main challenge of the project was unstructured input siloed in business systems. Our team of engineers built a serverless infrastructure on Azure for real-time analysis and intelligent reporting.
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.
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
Azure data lake analytics pricing varies and is based on the Pay-as-You-Go model and monthly commitment packages.
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.
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.
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.
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.