We are using the solution for call monitoring and connecting Data Lakes. We have different data at various locations, both structured and unstructured, which we use for analytics.
Data Engineer at a financial services firm with 1,001-5,000 employees
Real User
Top 10
2024-11-15T20:04:00Z
Nov 15, 2024
For our current use case, we develop a solution to do PDF extraction. We store PDF files in Azure Data Lake Storage. This helps our application in AI solutions, where users can query something and get results, and we can cross-check by showing the PDF files stored in the Data Lake Storage.
DevOps Manager at a computer software company with 5,001-10,000 employees
MSP
Top 5
2024-11-11T16:08:00Z
Nov 11, 2024
Data Lake Storage is primarily used to ingest different types of data such as file share data, raw data, or unstructured data. Additionally, the Data Lake Storage account is Gen Two and is integrated with various Azure resources such as Azure Cloud, Databricks, Data Factory, and SQL DB. Furthermore, it is also compatible with other solutions such as Cosmos DB, Stream Analytics, and Event Hubs.
We are restoring external tables and data in Databricks, accessing those tables to read and write data using Azure Data Lake Storage. We use it for data quality purposes and store data to form external tables.
Independent consultant at a hospitality company with 1-10 employees
Real User
Top 20
2024-09-11T03:09:00Z
Sep 11, 2024
We use the tool for multiple processes. We use it as a storage layer for files coming in from relational systems and data from real-time streaming systems. We also use it as a staging area for data scientists to consume.
We use Azure Data Lake Storage for sources like HubSpot (CRM software) and Xero (invoice software). We call their APIs, get the data, and store it in the product. From there, we use it to get the responses and load them into Azure SQL DB.
Data Engineer at Universidad Peruana de Ciencias Aplicadas
Real User
Top 20
2024-05-10T15:48:00Z
May 10, 2024
We have parameters to create three types of data storage. The first is staging, the second is the intermediate, and the third is the target. The target storage contains the cleanest data used for reporting tools. For specific cases in Data Lake projects, we often use files stored in formats such as CSV, which are the most useful for this type of data processing.
I use the solution in my company as per our project requirements. In my company, we are only putting data from on-premises RDBMS into Azure Data Lake Storage Gen2, and then the file is stored in parquet format. After the aforementioned process is followed, my company has another data engineering team, which reads those data further.
We use the Azure Data Lake to store raw customer files exported from their databases. Our pipelines then pick up this data and process it in various ways. For instance, we use Databricks to handle the data processing, transformation, and ETL tasks. The processed data is then stored in SQL Server or converted into other file formats.
Azure Data Lake Storage is widely used for data warehousing, storing processed data, raw customer files, and integrating data from multiple sources, supporting analytics, reporting, and machine learning by securely storing JSON, CSV, and other formats.
Organizations use Azure Data Lake Storage to aggregate information for reporting, integrate it into data pipelines, and benefit from secure transfer capabilities. It serves data scientists as a staging area and businesses leverage its Big...
We use Azure Data Lake Storage for managing large data volumes in our big data projects.
We are using the solution for call monitoring and connecting Data Lakes. We have different data at various locations, both structured and unstructured, which we use for analytics.
For our current use case, we develop a solution to do PDF extraction. We store PDF files in Azure Data Lake Storage. This helps our application in AI solutions, where users can query something and get results, and we can cross-check by showing the PDF files stored in the Data Lake Storage.
Data Lake Storage is primarily used to ingest different types of data such as file share data, raw data, or unstructured data. Additionally, the Data Lake Storage account is Gen Two and is integrated with various Azure resources such as Azure Cloud, Databricks, Data Factory, and SQL DB. Furthermore, it is also compatible with other solutions such as Cosmos DB, Stream Analytics, and Event Hubs.
We are restoring external tables and data in Databricks, accessing those tables to read and write data using Azure Data Lake Storage. We use it for data quality purposes and store data to form external tables.
We use the tool for multiple processes. We use it as a storage layer for files coming in from relational systems and data from real-time streaming systems. We also use it as a staging area for data scientists to consume.
We use Azure Data Lake Storage for sources like HubSpot (CRM software) and Xero (invoice software). We call their APIs, get the data, and store it in the product. From there, we use it to get the responses and load them into Azure SQL DB.
It is typically used in my data analytics workflows. I use it as a data lake.
We have parameters to create three types of data storage. The first is staging, the second is the intermediate, and the third is the target. The target storage contains the cleanest data used for reporting tools. For specific cases in Data Lake projects, we often use files stored in formats such as CSV, which are the most useful for this type of data processing.
I use the solution in my company as per our project requirements. In my company, we are only putting data from on-premises RDBMS into Azure Data Lake Storage Gen2, and then the file is stored in parquet format. After the aforementioned process is followed, my company has another data engineering team, which reads those data further.
We use the Azure Data Lake to store raw customer files exported from their databases. Our pipelines then pick up this data and process it in various ways. For instance, we use Databricks to handle the data processing, transformation, and ETL tasks. The processed data is then stored in SQL Server or converted into other file formats.