1620544014

The Tale of OLTP, OLAP, and HTAP in Data Warehousing - The Backend Engineering Show

In this show, I discuss why we have 3 data models in database systems, OLTP (Online Transactional Processing) OLAP (Online Analytical Processing), and HTAP (Hybrid Transactional Analytical Processing). I’ll also explain the difference between them, the use of ETL tools (extract transform load) to load data from transactional to analytical databases, and what is the future of HTAP.

#developer

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The Tale of OLTP, OLAP, and HTAP in Data Warehousing - The Backend Engineering Show

Callum Slater

1653465344

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

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

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.

Gerhard Brink

1621413060

Top 5 Exciting Data Engineering Projects & Ideas For Beginners [2021]

Data engineering is among the core branches of big data. If you’re studying to become a data engineer and want some projects to showcase your skills (or gain knowledge), you’ve come to the right place. In this article, we’ll discuss data engineering project ideas you can work on and several data engineering projects, and you should be aware of it.

You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience.

Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.

That’s because you’ll need to complete the projects correctly. Here are the most important ones:

Python and its use in big data
Extract Transform Load (ETL) solutions
Hadoop and related big data technologies
Concept of data pipelines
Apache Airflow

#big data #big data projects #data engineer #data engineer project #data engineering projects #data projects

iOS App Dev

1624072920

10 Must-have Skills for Data Engineering Jobs

Big data skills are crucial to land up data engineering job roles. From designing, creating, building, and maintaining data pipelines to collating raw data from various sources and ensuring performance optimization, data engineering professionals carry a plethora of tasks. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and to name a few.

Here, we list some of the important skills that one should possess to build a successful career in big data.

1. Database Tools

2. Data Transformation Tools

3. Data Ingestion Tools

4. Data Mining Tools

#big data #latest news #data engineering jobs #skills for data engineering jobs #10 must-have skills for data engineering jobs #data engineering