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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to Handle Spaces in PySpark Dataframe Column

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In PySpark, you can employ SQL queries by importing your CSV file data to a DataFrame. However, you might face problems when dealing with spaces in column names of the DataFrame. Fortunately, there is a solution available to resolve this issue.


SQL Space in Column Names


Reading CSV file to Dataframe

Here is the PySpark code for reading CSV files and writing to a DataFrame.

#initiate session
spark = SparkSession.builder \
.appName("PySpark Tutorial") \
.getOrCreate()


#Read CSV file to df dataframe
data_path = '/content/Test1.csv'
df = spark.read.csv(data_path, header=True, inferSchema=True)

#Create a Temporary view for the DataFrame
df2.createOrReplaceTempView("temp_table")

#Read data from the temporary view
spark.sql("select * from temp_table").show()

Output
--------+-----+---------------+---+
|Student| Year|Semester1|Semester2|
| ID | | Marks | Marks |
+----------+-----+---------------+ | si1 |year1|62.08| 62.4| | si1 |year2|75.94| 76.75| | si2 |year1|68.26| 72.95| | si2 |year2|85.49| 75.8| | si3 |year1|75.08| 79.84| | si3 |year2|54.98| 87.72| | si4 |year1|50.03| 66.85| | si4 |year2|71.26| 69.77| | si5 |year1|52.74| 76.27| | si5 |year2|50.39| 68.58| | si6 |year1|74.86| 60.8| | si6 |year2|58.29| 62.38| | si7 |year1|63.95| 74.51| | si7 |year2|66.69| 56.92| +----------+-----+-------------+

Fix for space in the column name


Suppose the column name "Student ID" contains a space. To prevent errors, you must modify your SQL query.

spark.sql("select `Student ID` as sid from temp_table").show()

Output:

+---+ |sid| +---+ |si1| |si1| |si2| |si2| |si3| |si3| |si4| |si4| |si5| |si5| |si6| |si6| |si7| |si7| +---+


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