[Notebook Samples] Add metadata file & Remove invalid characters in samples

Notebooks/PySpark/05 Using Azure Open Datasets in Synapse.ipynb

@@ -270,7 +270,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Enrich with weather data¶\n",
+        "## Enrich with weather data\n",
         "\n",
         "Now we append NOAA surface weather data to the taxi and holiday data. Use a similar approach to fetch the [NOAA weather history data](https://azure.microsoft.com/en-us/services/open-datasets/catalog/noaa-integrated-surface-data/) from Azure Open Datasets. "
       ],

Notebooks/PySpark/07 Data Exploration and ML Modeling - NYC taxi predict using Spark MLlib.ipynb

@@ -48,7 +48,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Ingest Data¶ \n",
+        "## Ingest Data\n",
         "\n",
         "Get a sample data of nyc yellow taxi to make it faster/easier to evaluate different approaches to prep for the modelling phase later in the notebook."
       ],

Notebooks/metadata.json

@@ -0,0 +1,68 @@
+{
+    "data": [
+        {
+            "title": "Using Azure Open Datasets in Synapse'",
+            "description": "This notebook provides examples of how to enrich NYC Green Taxi Data with Holiday and Weather.",
+            "path": "PySpark\\05 Using Azure Open Datasets in Synapse.ipynb",
+            "tags": ["Column projection", "Filtering", "Join", "Spark Table"],
+            "types": ["PySpark", "Sample"],
+            "languages": ["PySpark"]
+        },
+        {
+            "title": "Charting in Synapse Notebook",
+            "description": "This notebook provides examples to visualize data in Synapse notebook using matplotlib, bokeh, or seaborn.",
+            "path": "PySpark\\06 Charting in Synapse Notebook.ipynb",
+            "tags": ["Matplotlib", "Bokeh", "Seaborn", "Visualization", "Charting"],
+            "types": ["PySpark", "Sample"],
+            "languages": ["PySpark"]
+        },
+        {
+            "title": "Data Exploration and ML Modeling - NYC taxi predict using Spark MLlib",
+            "description": "The notebook ingests, visualizes, prepares and then trains a model based on an Open Dataset that tracks NYC Yellow Taxi trips and various attributes around them. The goal is to predict for a given trip whether there will be a trip or not.",
+            "path": "PySpark\\07 Data Exploration and ML Modeling - NYC taxi predict using Spark MLlib.ipynb",
+            "tags": ["Spark MLlib", "Matplotlib", "Logistic Regression", "Machine Learning"],
+            "types": ["PySpark", "Sample"],
+            "languages": ["PySpark"]
+        },
+        {
+            "title": "Getting Started with Hyperspace Indexing",
+            "description": "This notebook shows you a tour of using Hyperspace indexing and how you could accelerate your Apache Spark workloads.",
+            "path": "PySpark\\Hitchhikers Guide to Hyperspace - Python.ipynb",
+            "tags": ["Spark", "Hyperspace Indexing", "Python", "PySpark", "Incremental", "Hybrid-Scan", "Accelerate", "Join", "Filter", "Acceleration", "Fast", "Parquet", "JSON", "CSV", "Workload"],
+            "types": ["PySpark", "Sample"],
+            "languages": ["PySpark"]
+        },
+        {
+            "title": "Getting Started with Hyperspace Indexing",
+            "description": "This notebook shows you a tour of using Hyperspace indexing and how you could accelerate your Apache Spark workloads.",
+            "path": "Scala\\Hitchhikers Guide to Hyperspace - Scala.ipynb",
+            "tags": ["Spark", "Hyperspace Indexing", "Scala", "Incremental", "Hybrid-Scan", "Accelerate", "Join", "Filter", "Acceleration", "Fast", "Parquet", "JSON", "CSV", "Workload"],
+            "types": ["Scala", "Sample"],
+            "languages": ["Scala"]
+        },
+        {
+            "title": "Experimenting with dotNET for Spark Using CreateDataFrame",
+            "description": "A Spark DataFrame is a distributed collection of data organized into named columns that provides operations to filter, group, or compute aggregates, and can be used with Spark SQL. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood.",
+            "path": "Spark.NET C#\\Experimenting with DotNet for Spark Using CreateDataFrame.ipynb",
+            "tags": ["C#", "Sample", "Spark.NET C#", "CreateDataFrame"],
+            "types": ["Spark.NET C#", "Sample"],
+            "languages": ["Spark.NET C#"]
+        },
+        {
+            "title": "Getting Started with Hyperspace Indexing",
+            "description": "This notebook shows you a tour of using Hyperspace indexing and how you could accelerate your Apache Spark workloads.",
+            "path": "Spark.NET C#\\Hitchhikers Guide to Hyperspace - CSharp.ipynb",
+            "tags": ["Spark", "Hyperspace Indexing", ".NET", "dotnet", "dotnet-core" , "Incremental", "Hybrid-Scan", "Accelerate", "Join", "Filter", "Acceleration", "Fast", "Parquet", "JSON", "CSV", "Workload"],
+            "types": ["Spark.NET C#", "Sample"],
+            "languages": ["Spark.NET C#"]
+        },
+        {
+            "title": "User-Defined Functions with Complex Types in dotNET for Spark",
+            "description": "This notebook explains how to construct UDFs in C# and includes example functions, such as how to use UDFs with complex Row objects.",
+            "path": "Spark.NET C#\\User-Defined Functions with Complex Types in DotNet for Spark.ipynb",
+            "tags": ["C#", "Sample", "Spark.NET C#", "User-Defined Functions"],
+            "types": ["Spark.NET C#", "Sample"],
+            "languages": ["Spark.NET C#"]
+        }
+    ]
+}