The New Gateway API with Mark Church of Google: DevOps and Docker Live Show

Starts at 3:40. I meet with Mark Church, Product Manager of GKE Networking at Google. We’ll be talking and taking questions about the new Gateway API and Kubernetes Ingress.

Support this show on Patreon! It’s the #1 way to support this show, my podcast, and open source https://patreon.com/BretFisher

Join the discussion on our Discord chat server https://devops.fan

#devops #kubernetes #api

PySpark Cheat Sheet: Spark DataFrames in Python

This PySpark SQL cheat sheet is your handy companion to Apache Spark DataFrames in Python and includes code samples.

You'll probably already know about Apache Spark, the fast, general and open-source engine for big data processing; It has built-in modules for streaming, SQL, machine learning and graph processing. Spark allows you to speed analytic applications up to 100 times faster compared to other technologies on the market today. Interfacing Spark with Python is easy with PySpark: this Spark Python API exposes the Spark programming model to Python.

Now, it's time to tackle the Spark SQL module, which is meant for structured data processing, and the DataFrame API, which is not only available in Python, but also in Scala, Java, and R.

Without further ado, here's the cheat sheet:

PySpark SQL cheat sheet

This PySpark SQL cheat sheet covers the basics of working with the Apache Spark DataFrames in Python: from initializing the SparkSession to creating DataFrames, inspecting the data, handling duplicate values, querying, adding, updating or removing columns, grouping, filtering or sorting data. You'll also see that this cheat sheet also on how to run SQL Queries programmatically, how to save your data to parquet and JSON files, and how to stop your SparkSession.

Spark SGlL is Apache Spark's module for working with structured data.

Initializing SparkSession

A SparkSession can be used create DataFrame, register DataFrame as tables, execute SGL over tables, cache tables, and read parquet files.

>>> from pyspark.sql import SparkSession
>>> spark a SparkSession \
     .builder\
     .appName("Python Spark SQL basic example") \
     .config("spark.some.config.option", "some-value") \
     .getOrCreate()

Creating DataFrames

Fromm RDDs

>>> from pyspark.sql.types import*

Infer Schema

>>> sc = spark.sparkContext
>>> lines = sc.textFile(''people.txt'')
>>> parts = lines.map(lambda l: l.split(","))
>>> people = parts.map(lambda p: Row(nameap[0],ageaint(p[l])))
>>> peopledf = spark.createDataFrame(people)

Specify Schema

>>> people = parts.map(lambda p: Row(name=p[0],
               age=int(p[1].strip())))
>>>  schemaString = "name age"
>>> fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]
>>> schema = StructType(fields)
>>> spark.createDataFrame(people, schema).show()

From Spark Data Sources
JSON

>>>  df = spark.read.json("customer.json")
>>> df.show()

>>>  df2 = spark.read.load("people.json", format="json")

Parquet files

>>> df3 = spark.read.load("users.parquet")

TXT files

>>> df4 = spark.read.text("people.txt")

Filter

#Filter entries of age, only keep those records of which the values are >24
>>> df.filter(df["age"]>24).show()

Duplicate Values

>>> df = df.dropDuplicates()

Queries

>>> from pyspark.sql import functions as F

Select

>>> df.select("firstName").show() #Show all entries in firstName column
>>> df.select("firstName","lastName") \
      .show()
>>> df.select("firstName", #Show all entries in firstName, age and type
              "age",
              explode("phoneNumber") \
              .alias("contactInfo")) \
      .select("contactInfo.type",
              "firstName",
              "age") \
      .show()
>>> df.select(df["firstName"],df["age"]+ 1) #Show all entries in firstName and age, .show() add 1 to the entries of age
>>> df.select(df['age'] > 24).show() #Show all entries where age >24

When

>>> df.select("firstName", #Show firstName and 0 or 1 depending on age >30
               F.when(df.age > 30, 1) \
              .otherwise(0)) \
      .show()
>>> df[df.firstName.isin("Jane","Boris")] #Show firstName if in the given options
.collect()

>>> df.select("firstName", #Show firstName, and lastName is TRUE if lastName is like Smith
              df.lastName.like("Smith")) \
     .show()

Startswith - Endswith

>>> df.select("firstName", #Show firstName, and TRUE if lastName starts with Sm
              df.lastName \
                .startswith("Sm")) \
      .show()
>>> df.select(df.lastName.endswith("th"))\ #Show last names ending in th
      .show()

Substring

>>> df.select(df.firstName.substr(1, 3) \ #Return substrings of firstName
                          .alias("name")) \
        .collect()

Between

>>> df.select(df.age.between(22, 24)) \ #Show age: values are TRUE if between 22 and 24
          .show()

Add, Update & Remove Columns

Adding Columns

 >>> df = df.withColumn('city',df.address.city) \
            .withColumn('postalCode',df.address.postalCode) \
            .withColumn('state',df.address.state) \
            .withColumn('streetAddress',df.address.streetAddress) \
            .withColumn('telePhoneNumber', explode(df.phoneNumber.number)) \
            .withColumn('telePhoneType', explode(df.phoneNumber.type))

Updating Columns

>>> df = df.withColumnRenamed('telePhoneNumber', 'phoneNumber')

Removing Columns

  >>> df = df.drop("address", "phoneNumber")
 >>> df = df.drop(df.address).drop(df.phoneNumber)

Missing & Replacing Values

>>> df.na.fill(50).show() #Replace null values
 >>> df.na.drop().show() #Return new df omitting rows with null values
 >>> df.na \ #Return new df replacing one value with another
       .replace(10, 20) \
       .show()

GroupBy

>>> df.groupBy("age")\ #Group by age, count the members in the groups
      .count() \
      .show()

Sort

>>> peopledf.sort(peopledf.age.desc()).collect()
>>> df.sort("age", ascending=False).collect()
>>> df.orderBy(["age","city"],ascending=[0,1])\
     .collect()

Repartitioning

>>> df.repartition(10)\ #df with 10 partitions
      .rdd \
      .getNumPartitions()
>>> df.coalesce(1).rdd.getNumPartitions() #df with 1 partition

Running Queries Programmatically

Registering DataFrames as Views

>>> peopledf.createGlobalTempView("people")
>>> df.createTempView("customer")
>>> df.createOrReplaceTempView("customer")

Query Views

>>> df5 = spark.sql("SELECT * FROM customer").show()
>>> peopledf2 = spark.sql("SELECT * FROM global_temp.people")\
               .show()

Inspect Data

>>> df.dtypes #Return df column names and data types
>>> df.show() #Display the content of df
>>> df.head() #Return first n rows
>>> df.first() #Return first row
>>> df.take(2) #Return the first n rows >>> df.schema Return the schema of df
>>> df.describe().show() #Compute summary statistics >>> df.columns Return the columns of df
>>> df.count() #Count the number of rows in df
>>> df.distinct().count() #Count the number of distinct rows in df
>>> df.printSchema() #Print the schema of df
>>> df.explain() #Print the (logical and physical) plans

Output

Data Structures

 >>> rdd1 = df.rdd #Convert df into an RDD
 >>> df.toJSON().first() #Convert df into a RDD of string
 >>> df.toPandas() #Return the contents of df as Pandas DataFrame

Write & Save to Files

>>> df.select("firstName", "city")\
       .write \
       .save("nameAndCity.parquet")
 >>> df.select("firstName", "age") \
       .write \
       .save("namesAndAges.json",format="json")

Stopping SparkSession

>>> spark.stop()

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#pyspark #cheatsheet #spark #dataframes #python #bigdata

Autumn Blick

1601381326

Public ASX100 APIs: The Essential List

We’ve conducted some initial research into the public APIs of the ASX100 because we regularly have conversations about what others are doing with their APIs and what best practices look like. Being able to point to good local examples and explain what is happening in Australia is a key part of this conversation.

Method

The method used for this initial research was to obtain a list of the ASX100 (as of 18 September 2020). Then work through each company looking at the following:

Whether the company had a public API: this was found by googling “[company name] API” and “[company name] API developer” and “[company name] developer portal”. Sometimes the company’s website was navigated or searched.
Some data points about the API were noted, such as the URL of the portal/documentation and the method they used to publish the API (portal, documentation, web page).
Observations were recorded that piqued the interest of the researchers (you will find these below).
Other notes were made to support future research.
You will find a summary of the data in the infographic below.

Data

With regards to how the APIs are shared:

Documentation is where just the Swagger/OpenAPI docs are hosted somewhere. e.g. https://api.linkgroup.com/member/docs/index
Portal is where an interactive portal has been developed. e.g. https://dev.telstra.com/
The web page is where the company has a single page that isn’t interactive explaining their Public APIs. e.g. https://www.bendigoadelaide.com.au/banking-products-api.asp

#api #api-development #api-analytics #apis #api-integration #api-testing #api-security #api-gateway

Robert E. Hilton

1642496884

How to Create a Responsive Dropdown Menu Bar with Search Field using HTML & CSS

In this guide you’ll learn how to create a Responsive Dropdown Menu Bar with Search Field using only HTML & CSS.

To create a responsive dropdown menu bar with search field using only HTML & CSS . First, you need to create two Files one HTML File and another one is CSS File.

1: First, create an HTML file with the name of index.html

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta http-equiv="X-UA-Compatible" content="ie=edge">
  <title>Dropdown Menu with Search Box | Codequs</title>
  <link rel="stylesheet" href="style.css">
  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"/>
</head>
<body>
  <div class="wrapper">
    <nav>
      <input type="checkbox" id="show-search">
      <input type="checkbox" id="show-menu">
      <label for="show-menu" class="menu-icon"><i class="fas fa-bars"></i></label>
      <div class="content">
      <div class="logo"><a href="#">CodingNepal</a></div>
        <ul class="links">
          <li><a href="#">Home</a></li>
          <li><a href="#">About</a></li>
          <li>
            <a href="#" class="desktop-link">Features</a>
            <input type="checkbox" id="show-features">
            <label for="show-features">Features</label>
            <ul>
              <li><a href="#">Drop Menu 1</a></li>
              <li><a href="#">Drop Menu 2</a></li>
              <li><a href="#">Drop Menu 3</a></li>
              <li><a href="#">Drop Menu 4</a></li>
            </ul>
          </li>
          <li>
            <a href="#" class="desktop-link">Services</a>
            <input type="checkbox" id="show-services">
            <label for="show-services">Services</label>
            <ul>
              <li><a href="#">Drop Menu 1</a></li>
              <li><a href="#">Drop Menu 2</a></li>
              <li><a href="#">Drop Menu 3</a></li>
              <li>
                <a href="#" class="desktop-link">More Items</a>
                <input type="checkbox" id="show-items">
                <label for="show-items">More Items</label>
                <ul>
                  <li><a href="#">Sub Menu 1</a></li>
                  <li><a href="#">Sub Menu 2</a></li>
                  <li><a href="#">Sub Menu 3</a></li>
                </ul>
              </li>
            </ul>
          </li>
          <li><a href="#">Feedback</a></li>
        </ul>
      </div>
      <label for="show-search" class="search-icon"><i class="fas fa-search"></i></label>
      <form action="#" class="search-box">
        <input type="text" placeholder="Type Something to Search..." required>
        <button type="submit" class="go-icon"><i class="fas fa-long-arrow-alt-right"></i></button>
      </form>
    </nav>
  </div>
  <div class="dummy-text">
    <h2>Responsive Dropdown Menu Bar with Searchbox</h2>
    <h2>using only HTML & CSS - Flexbox</h2>
  </div>
</body>
</html>

2: Second, create a CSS file with the name of style.css

@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@200;300;400;500;600;700&display=swap');
*{
  margin: 0;
  padding: 0;
  box-sizing: border-box;
  text-decoration: none;
  font-family: 'Poppins', sans-serif;
}
.wrapper{
  background: #171c24;
  position: fixed;
  width: 100%;
}
.wrapper nav{
  position: relative;
  display: flex;
  max-width: calc(100% - 200px);
  margin: 0 auto;
  height: 70px;
  align-items: center;
  justify-content: space-between;
}
nav .content{
  display: flex;
  align-items: center;
}
nav .content .links{
  margin-left: 80px;
  display: flex;
}
.content .logo a{
  color: #fff;
  font-size: 30px;
  font-weight: 600;
}
.content .links li{
  list-style: none;
  line-height: 70px;
}
.content .links li a,
.content .links li label{
  color: #fff;
  font-size: 18px;
  font-weight: 500;
  padding: 9px 17px;
  border-radius: 5px;
  transition: all 0.3s ease;
}
.content .links li label{
  display: none;
}
.content .links li a:hover,
.content .links li label:hover{
  background: #323c4e;
}
.wrapper .search-icon,
.wrapper .menu-icon{
  color: #fff;
  font-size: 18px;
  cursor: pointer;
  line-height: 70px;
  width: 70px;
  text-align: center;
}
.wrapper .menu-icon{
  display: none;
}
.wrapper #show-search:checked ~ .search-icon i::before{
  content: "\f00d";
}
.wrapper .search-box{
  position: absolute;
  height: 100%;
  max-width: calc(100% - 50px);
  width: 100%;
  opacity: 0;
  pointer-events: none;
  transition: all 0.3s ease;
}
.wrapper #show-search:checked ~ .search-box{
  opacity: 1;
  pointer-events: auto;
}
.search-box input{
  width: 100%;
  height: 100%;
  border: none;
  outline: none;
  font-size: 17px;
  color: #fff;
  background: #171c24;
  padding: 0 100px 0 15px;
}
.search-box input::placeholder{
  color: #f2f2f2;
}
.search-box .go-icon{
  position: absolute;
  right: 10px;
  top: 50%;
  transform: translateY(-50%);
  line-height: 60px;
  width: 70px;
  background: #171c24;
  border: none;
  outline: none;
  color: #fff;
  font-size: 20px;
  cursor: pointer;
}
.wrapper input[type="checkbox"]{
  display: none;
}
/* Dropdown Menu code start */
.content .links ul{
  position: absolute;
  background: #171c24;
  top: 80px;
  z-index: -1;
  opacity: 0;
  visibility: hidden;
}
.content .links li:hover > ul{
  top: 70px;
  opacity: 1;
  visibility: visible;
  transition: all 0.3s ease;
}
.content .links ul li a{
  display: block;
  width: 100%;
  line-height: 30px;
  border-radius: 0px!important;
}
.content .links ul ul{
  position: absolute;
  top: 0;
  right: calc(-100% + 8px);
}
.content .links ul li{
  position: relative;
}
.content .links ul li:hover ul{
  top: 0;
}
/* Responsive code start */
@media screen and (max-width: 1250px){
  .wrapper nav{
    max-width: 100%;
    padding: 0 20px;
  }
  nav .content .links{
    margin-left: 30px;
  }
  .content .links li a{
    padding: 8px 13px;
  }
  .wrapper .search-box{
    max-width: calc(100% - 100px);
  }
  .wrapper .search-box input{
    padding: 0 100px 0 15px;
  }
}
@media screen and (max-width: 900px){
  .wrapper .menu-icon{
    display: block;
  }
  .wrapper #show-menu:checked ~ .menu-icon i::before{
    content: "\f00d";
  }
  nav .content .links{
    display: block;
    position: fixed;
    background: #14181f;
    height: 100%;
    width: 100%;
    top: 70px;
    left: -100%;
    margin-left: 0;
    max-width: 350px;
    overflow-y: auto;
    padding-bottom: 100px;
    transition: all 0.3s ease;
  }
  nav #show-menu:checked ~ .content .links{
    left: 0%;
  }
  .content .links li{
    margin: 15px 20px;
  }
  .content .links li a,
  .content .links li label{
    line-height: 40px;
    font-size: 20px;
    display: block;
    padding: 8px 18px;
    cursor: pointer;
  }
  .content .links li a.desktop-link{
    display: none;
  }
  /* dropdown responsive code start */
  .content .links ul,
  .content .links ul ul{
    position: static;
    opacity: 1;
    visibility: visible;
    background: none;
    max-height: 0px;
    overflow: hidden;
  }
  .content .links #show-features:checked ~ ul,
  .content .links #show-services:checked ~ ul,
  .content .links #show-items:checked ~ ul{
    max-height: 100vh;
  }
  .content .links ul li{
    margin: 7px 20px;
  }
  .content .links ul li a{
    font-size: 18px;
    line-height: 30px;
    border-radius: 5px!important;
  }
}
@media screen and (max-width: 400px){
  .wrapper nav{
    padding: 0 10px;
  }
  .content .logo a{
    font-size: 27px;
  }
  .wrapper .search-box{
    max-width: calc(100% - 70px);
  }
  .wrapper .search-box .go-icon{
    width: 30px;
    right: 0;
  }
  .wrapper .search-box input{
    padding-right: 30px;
  }
}
.dummy-text{
  position: absolute;
  top: 50%;
  left: 50%;
  width: 100%;
  z-index: -1;
  padding: 0 20px;
  text-align: center;
  transform: translate(-50%, -50%);
}
.dummy-text h2{
  font-size: 45px;
  margin: 5px 0;
}

Now you’ve successfully created a Responsive Dropdown Menu Bar with Search Field using only HTML & CSS.

Alice Klocko

1617932628

The New Gateway API with Mark Church of Google: DevOps and Docker Live Show

Starts at 3:40. I meet with Mark Church, Product Manager of GKE Networking at Google. We’ll be talking and taking questions about the new Gateway API and Kubernetes Ingress.

Support this show on Patreon! It’s the #1 way to support this show, my podcast, and open source https://patreon.com/BretFisher

Join the discussion on our Discord chat server https://devops.fan

#devops #kubernetes #api

Domingo Jakubowski

1595396220

Top 10 API Security Threats Every API Team Should Know

As more and more data is exposed via APIs either as API-first companies or for the explosion of single page apps/JAMStack, API security can no longer be an afterthought. The hard part about APIs is that it provides direct access to large amounts of data while bypassing browser precautions. Instead of worrying about SQL injection and XSS issues, you should be concerned about the bad actor who was able to paginate through all your customer records and their data.

Typical prevention mechanisms like Captchas and browser fingerprinting won’t work since APIs by design need to handle a very large number of API accesses even by a single customer. So where do you start? The first thing is to put yourself in the shoes of a hacker and then instrument your APIs to detect and block common attacks along with unknown unknowns for zero-day exploits. Some of these are on the OWASP Security API list, but not all.

Insecure pagination and resource limits

Most APIs provide access to resources that are lists of entities such as /users or /widgets. A client such as a browser would typically filter and paginate through this list to limit the number items returned to a client like so:

First Call: GET /items?skip=0&take=10 
Second Call: GET /items?skip=10&take=10

However, if that entity has any PII or other information, then a hacker could scrape that endpoint to get a dump of all entities in your database. This could be most dangerous if those entities accidently exposed PII or other sensitive information, but could also be dangerous in providing competitors or others with adoption and usage stats for your business or provide scammers with a way to get large email lists. See how Venmo data was scraped

A naive protection mechanism would be to check the take count and throw an error if greater than 100 or 1000. The problem with this is two-fold:

For data APIs, legitimate customers may need to fetch and sync a large number of records such as via cron jobs. Artificially small pagination limits can force your API to be very chatty decreasing overall throughput. Max limits are to ensure memory and scalability requirements are met (and prevent certain DDoS attacks), not to guarantee security.
This offers zero protection to a hacker that writes a simple script that sleeps a random delay between repeated accesses.

skip = 0
while True:    response = requests.post('https://api.acmeinc.com/widgets?take=10&skip=' + skip),                      headers={'Authorization': 'Bearer' + ' ' + sys.argv[1]})    print("Fetched 10 items")    sleep(randint(100,1000))    skip += 10

How to secure against pagination attacks

To secure against pagination attacks, you should track how many items of a single resource are accessed within a certain time period for each user or API key rather than just at the request level. By tracking API resource access at the user level, you can block a user or API key once they hit a threshold such as “touched 1,000,000 items in a one hour period”. This is dependent on your API use case and can even be dependent on their subscription with you. Like a Captcha, this can slow down the speed that a hacker can exploit your API, like a Captcha if they have to create a new user account manually to create a new API key.

Insecure API key generation

Most APIs are protected by some sort of API key or JWT (JSON Web Token). This provides a natural way to track and protect your API as API security tools can detect abnormal API behavior and block access to an API key automatically. However, hackers will want to outsmart these mechanisms by generating and using a large pool of API keys from a large number of users just like a web hacker would use a large pool of IP addresses to circumvent DDoS protection.

How to secure against API key pools

The easiest way to secure against these types of attacks is by requiring a human to sign up for your service and generate API keys. Bot traffic can be prevented with things like Captcha and 2-Factor Authentication. Unless there is a legitimate business case, new users who sign up for your service should not have the ability to generate API keys programmatically. Instead, only trusted customers should have the ability to generate API keys programmatically. Go one step further and ensure any anomaly detection for abnormal behavior is done at the user and account level, not just for each API key.

Accidental key exposure

APIs are used in a way that increases the probability credentials are leaked:

APIs are expected to be accessed over indefinite time periods, which increases the probability that a hacker obtains a valid API key that’s not expired. You save that API key in a server environment variable and forget about it. This is a drastic contrast to a user logging into an interactive website where the session expires after a short duration.
The consumer of an API has direct access to the credentials such as when debugging via Postman or CURL. It only takes a single developer to accidently copy/pastes the CURL command containing the API key into a public forum like in GitHub Issues or Stack Overflow.
API keys are usually bearer tokens without requiring any other identifying information. APIs cannot leverage things like one-time use tokens or 2-factor authentication.

If a key is exposed due to user error, one may think you as the API provider has any blame. However, security is all about reducing surface area and risk. Treat your customer data as if it’s your own and help them by adding guards that prevent accidental key exposure.

How to prevent accidental key exposure

The easiest way to prevent key exposure is by leveraging two tokens rather than one. A refresh token is stored as an environment variable and can only be used to generate short lived access tokens. Unlike the refresh token, these short lived tokens can access the resources, but are time limited such as in hours or days.

The customer will store the refresh token with other API keys. Then your SDK will generate access tokens on SDK init or when the last access token expires. If a CURL command gets pasted into a GitHub issue, then a hacker would need to use it within hours reducing the attack vector (unless it was the actual refresh token which is low probability)

Exposure to DDoS attacks

APIs open up entirely new business models where customers can access your API platform programmatically. However, this can make DDoS protection tricky. Most DDoS protection is designed to absorb and reject a large number of requests from bad actors during DDoS attacks but still need to let the good ones through. This requires fingerprinting the HTTP requests to check against what looks like bot traffic. This is much harder for API products as all traffic looks like bot traffic and is not coming from a browser where things like cookies are present.

Stopping DDoS attacks

The magical part about APIs is almost every access requires an API Key. If a request doesn’t have an API key, you can automatically reject it which is lightweight on your servers (Ensure authentication is short circuited very early before later middleware like request JSON parsing). So then how do you handle authenticated requests? The easiest is to leverage rate limit counters for each API key such as to handle X requests per minute and reject those above the threshold with a 429 HTTP response. There are a variety of algorithms to do this such as leaky bucket and fixed window counters.

Incorrect server security

APIs are no different than web servers when it comes to good server hygiene. Data can be leaked due to misconfigured SSL certificate or allowing non-HTTPS traffic. For modern applications, there is very little reason to accept non-HTTPS requests, but a customer could mistakenly issue a non HTTP request from their application or CURL exposing the API key. APIs do not have the protection of a browser so things like HSTS or redirect to HTTPS offer no protection.

How to ensure proper SSL

Test your SSL implementation over at Qualys SSL Test or similar tool. You should also block all non-HTTP requests which can be done within your load balancer. You should also remove any HTTP headers scrub any error messages that leak implementation details. If your API is used only by your own apps or can only be accessed server-side, then review Authoritative guide to Cross-Origin Resource Sharing for REST APIs

Incorrect caching headers

APIs provide access to dynamic data that’s scoped to each API key. Any caching implementation should have the ability to scope to an API key to prevent cross-pollution. Even if you don’t cache anything in your infrastructure, you could expose your customers to security holes. If a customer with a proxy server was using multiple API keys such as one for development and one for production, then they could see cross-pollinated data.

#api management #api security #api best practices #api providers #security analytics #api management policies #api access tokens #api access #api security risks #api access keys

The New Gateway API with Mark Church of Google: DevOps and Docker Live Show

What is GEEK

Buddha Community

PySpark Cheat Sheet: Spark DataFrames in Python

Initializing SparkSession

Creating DataFrames

Fromm RDDs

Filter

Duplicate Values

Queries

Add, Update & Remove Columns

Missing & Replacing Values

GroupBy

Sort

Repartitioning

Running Queries Programmatically

Inspect Data

Output

Stopping SparkSession

Public ASX100 APIs: The Essential List

Method

Data

How to Create a Responsive Dropdown Menu Bar with Search Field using HTML & CSS

The New Gateway API with Mark Church of Google: DevOps and Docker Live Show

Top 10 API Security Threats Every API Team Should Know

Insecure pagination and resource limits

How to secure against pagination attacks

Insecure API key generation

How to secure against API key pools

Accidental key exposure

How to prevent accidental key exposure

Exposure to DDoS attacks

Stopping DDoS attacks

Incorrect server security

How to ensure proper SSL

Incorrect caching headers