Azure batch telemetry data inject to dstabade - azure

Question

i have to develop iot solution for now i have telemetry data coming as batch Data combining all devices data around 300 devices at once in a particular interval it is not live.what is the best way to design this.sending batch Data is also we can define

Answer 1

Hummm, I just answered another question similar to this one. Using the code below, we can merge data from multiple files, all with a similar name, into a data frame and push the whole thing into SQL Server. This is Scala, so it needs to be run in your Azure Databricks environment.
# merge files with similar names into a single dataframe
val DF = spark.read.format("csv")
.option("sep","|")
.option("inferSchema","true")
.option("header","false")
.load("mnt/rawdata/corp/ABC*.gz")
DF.count()
# rename headers in dataframe
val newNames = Seq("ID", "FName", "LName", "Address", "ZipCode", "file_name")
val dfRenamed = df.toDF(newNames: _*)
dfRenamed.printSchema
# push the dataframe to sql server
import com.microsoft.azure.sqldb.spark.config.Config
import com.microsoft.azure.sqldb.spark.connect._
// Aquire a DataFrame collection (val collection)
val config = Config(Map(
"url" -> "my_sql_server.database.windows.net",
"databaseName" -> "my_db_name",
"dbTable" -> "dbo.my_table",
"user" -> "xxxxx",
"password" -> "xxxxx",
"connectTimeout" -> "5", //seconds
"queryTimeout" -> "5" //seconds
))
import org.apache.spark.sql.SaveMode
DF.write.mode(SaveMode.Append).sqlDB(config)
The code above will read every line of every file. If the headers are in the first line, this works great. If the headers and NOT in the first line, use the code below to crate a specific schema, and again, read every line of every file.
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType};
import org.apache.spark.sql.functions.input_file_name
val customSchema = StructType(Array(
StructField("field1", StringType, true),
StructField("field2", StringType, true),
StructField("field3", StringType, true),
StructField("field4", StringType, true),
StructField("field5", StringType, true),
StructField("field6", StringType, true),
StructField("field7", StringType, true)))
val df = sqlContext.read
.format("com.databricks.spark.csv")
.option("header", "false")
.option("sep", "|")
.schema(customSchema)
.load("mnt/rawdata/corp/ABC*.gz")
.withColumn("file_name", input_file_name())
import com.microsoft.azure.sqldb.spark.bulkcopy.BulkCopyMetadata
import com.microsoft.azure.sqldb.spark.config.Config
import com.microsoft.azure.sqldb.spark.connect._
val bulkCopyConfig = Config(Map(
"url" -> "mysqlserver.database.windows.net",
"databaseName" -> "MyDatabase",
"user" -> "username",
"password" -> "*********",
"databaseName" -> "MyDatabase",
"dbTable" -> "dbo.Clients",
"bulkCopyBatchSize" -> "2500",
"bulkCopyTableLock" -> "true",
"bulkCopyTimeout" -> "600"
))
df.write.mode(SaveMode.Append).
//df.bulkCopyToSqlDB(bulkCopyConfig, bulkCopyMetadata)
//df.bulkCopyToSqlDB(bulkCopyConfig) if no metadata is specified.