How to use Azure Synapse Analytics without SQL Server at the back

Question

We have some files stored after doing some processing on them through Azure Synapse Workflow pipeline on Azure blob storage and we want to query them without using SQL Server at the back from R application. So how to achieve the same without SQL Server?

Thanks in advance.

Answer 1

Querying data directly from Azure Blob Storage within an R application, without using SQL Server, can be achieved by utilizing Azure Storage libraries and appropriate R packages to read and manipulate the data. The general process can be broken down into the following steps:

1. **Access Azure Blob Storage**: Utilize Azure Storage SDKs or R packages that allow you to connect to Azure Blob Storage. You can authenticate with the necessary credentials.

2. **Retrieve the Desired Files**: Once connected, you can list and retrieve the files from the blob storage. The files might be in formats like CSV, Parquet, JSON, etc.

3. **Read the Files into R**: Depending on the file format, you can use appropriate R functions or libraries to read the data into an R data frame.

4. **Query and Analyze the Data**: Once the data is in an R data frame, you can use standard R functions and packages to query, filter, and analyze the data as needed.

Below are more detailed steps and code snippets to guide you through this process:

### Step 1: Install Required Libraries
Install the necessary R packages. For example, you might use the `AzureStor` package to interact with Azure Blob Storage, and `readr` or `arrow` for reading CSV or Parquet files, respectively.

```R
install.packages("AzureStor")
install.packages("readr") # for CSV
install.packages("arrow") # for Parquet

Step 2: Authenticate and Connect to Azure Blob Storage

Utilize the AzureStor package to authenticate and connect to the Azure Blob Storage.

library(AzureStor)

storage_account_name <- "your_storage_account_name"
storage_account_key <- "your_storage_account_key"

endpoint <- storage_endpoint(
  sprintf("https://%s.blob.core.windows.net", storage_account_name),
  key=storage_account_key
)

container <- storage_container(endpoint, "your_container_name")

Step 3: Retrieve and Read Files

List the blobs and retrieve the file you want to query, then read it into an R data frame.

For CSV files:

library(readr)

blobs <- list_blobs(container)
blob <- get_blob(container, "path/to/yourfile.csv")
data <- read_csv(blob$content)

For Parquet files:

library(arrow)

blobs <- list_blobs(container)
blob <- get_blob(container, "path/to/yourfile.parquet")
data <- read_parquet(blob$content)

Step 4: Query the Data

Now that the data is in an R data frame, you can query it using standard R functions like subset, aggregate, etc.

# Example of filtering data
filtered_data <- subset(data, some_column > 100)

This approach bypasses the need for SQL Server by directly connecting to Azure Blob Storage and retrieving the files to be processed within R. Make sure that you follow best practices for security and compliance, particularly when handling authentication credentials.

Furthermore, depending on the complexity and size of the data, this method might face performance limitations. If frequent, complex querying is required, you might want to consider other Azure services like Azure Data Lake, which provide more robust querying capabilities. But for many scenarios, the approach outlined above should be sufficient.