# 创建 DataFramedf=spark.createDataFrame([(1,"Alice"),(2,"Bob"),(3,"Charlie")],["id","name"])# 创建视图df.createOrReplaceTempView("my_view") 1. 2. 3. 4. 5. 使用createOrReplaceView方法的优势是: 不需要提前创建表格结构:在使用createOrReplaceView方法时,不需要提前定义表格的结构,只需...
Prism is an open-source data orchestration platform designed for rapid development and robust deployment. Users can easily create, manage, and execute DAGs in Python, PySpark, and SQL.
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing the VectorAssembler from PySpark ML to...
First, let’s look at how we structured the training phase of our machine learning pipeline using PySpark: Training Notebook Connect to Eventhouse Load the data frompyspark.sqlimportSparkSession# Initialize Spark session (already set up in Fabric Notebooks)spark=SparkSession.builder.getOrCreate()#...
Navigate to Window > Show View > Other... > Sign In... From the Show View dialog, navigate to Azure > Azure Explorer, and then select Open. From Azure Explorer, right-click the Azure node, and then select Sign in. In the Azure Sign In dialog box, choose the authentication method,...
Work with cells in the Notebook Editor IntelliSense support in the Jupyter Notebook Editor View, inspect, and filter variables through the Variable explorer and Data viewer Debug a Jupyter Notebook Run Notebook against HDInsight clusters for PySpark query. ...
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing theVectorAssemblerfrom PySpark ML to co...
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing the VectorAssembler from PySpark ML to...
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing the VectorAssembler from PySpark ML...
These datasets are now ready for use in building and evaluating machine learning models. Python Copy # Import the necessary library for feature vectorization from pyspark.ml.feature import VectorAssembler # Load the cleaned and feature-engineered dataset from the lakehouse df_final = spark.read....