chore: update roadmap content json (#7120)

Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>

public/roadmap-content/git-github.json

@@ -13,6 +13,11 @@
         "url": "https://www.datacamp.com/blog/all-about-git",
         "type": "article"
       },
+      {
+        "title": "Version Control (Git) - The Missing Semester of Your CS Education",
+        "url": "https://missing.csail.mit.edu/2020/version-control/",
+        "type": "article"
+      },
       {
         "title": "GUI Clients",
         "url": "https://git-scm.com/downloads/guis",
@@ -215,6 +220,11 @@
     "title": ".gitignore",
     "description": "Ignored files are tracked in a special file named `.gitignore` that is checked in at the root of your repository. There is no explicit git ignore command: instead the `.gitignore` file must be edited and committed by hand when you have new files that you wish to ignore. `.gitignore` files contain patterns that are matched against file names in your repository to determine whether or not they should be ignored.\n\nVisit the following resources to learn more:",
     "links": [
+      {
+        "title": "gitignore - A collection of useful .gitignore templates",
+        "url": "https://github.com/github/gitignore",
+        "type": "opensource"
+      },
       {
         "title": "gitignore Documentation",
         "url": "https://git-scm.com/docs/gitignore/en",

public/roadmap-content/mlops.json

@@ -351,8 +351,13 @@
   },
   "UljuqA89_SlCSDWWMD_C_": {
     "title": "Spark",
-    "description": "Apache Spark is an open-source distributed computing system used for big data processing and analytics. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.\n\nVisit the following resources to learn more:",
+    "description": "Apache Spark is an open-source distributed computing system designed for big data processing and analytics. It offers a unified interface for programming entire clusters, enabling efficient handling of large-scale data with built-in support for data parallelism and fault tolerance. Spark excels in processing tasks like batch processing, real-time data streaming, machine learning, and graph processing. It’s known for its speed, ease of use, and ability to process data in-memory, significantly outperforming traditional MapReduce systems. Spark is widely used in big data ecosystems for its scalability and versatility across various data processing tasks.\n\nVisit the following resources to learn more:",
     "links": [
+      {
+        "title": "ApacheSpark",
+        "url": "https://spark.apache.org/documentation.html",
+        "type": "article"
+      },
       {
         "title": "Spark By Examples",
         "url": "https://sparkbyexamples.com",