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In the Big Data world, if you need:

Distributed Storage -> Apache Hadoop
Stream Processing -> Apache Kafka
Batch Data Processing -> Apache Spark
Real-Time Data Processing -> Spark Streaming
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Partitioning vs. Z-Ordering in Delta Lake

Partitioning:
Purpose: Partitioning divides data into separate directories based on the distinct values of a column (e.g., date, region, country). This helps in reducing the amount of data scanned during queries by only focusing on relevant partitions.
Example: Imagine you have a table storing sales data for multiple years:

CREATE TABLE sales_data
PARTITIONED BY (year)
AS
SELECT * FROM raw_data;

This creates a separate directory for each year (e.g., /year=2021/, /year=2022/). A query filtering on year can read only the relevant partition:

SELECT * FROM sales_data WHERE year = 2022;

Benefit: By scanning only the directory for the 2022 partition, the query is faster and avoids unnecessary I/O.

Usage: Ideal for columns with high cardinality or range-based queries like year, region, product_category.

Z-Ordering:

Purpose: Z-Ordering clusters data within the same file based on specific columns, allowing for efficient data skipping. This works well with columns frequently used in filtering or joining.
Example: Suppose you have a sales table partitioned by year, and you frequently run queries filtering by customer_id:

OPTIMIZE sales_data
ZORDER BY (customer_id);
Z-Ordering rearranges data within each partition so that rows with similar customer_id values are co-located. When you run a query with a filter:

SELECT * FROM sales_data WHERE customer_id = '12345';
Delta Lake skips irrelevant data, scanning fewer files and improving query speed.

Benefit: Reduces the number of rows/files that need to be scanned for queries with filter conditions.

Usage: Best used for columns often appearing in filters or joins like customer_id, product_id, zip_code. It works well when you already have partitioning in place.

Combined Approach:

Partition Data: First, partition your table based on key columns like date, region, or year for efficient range scans.
Apply Z-Ordering: Next, apply Z-Ordering within the partitions to cluster related data and enhance data skipping, e.g., partition by year and Z-Order by customer_id.

Example: If you have sales data partitioned by year and want to optimize queries filtering on product_id:

CREATE TABLE sales_data
PARTITIONED BY (year)
AS
SELECT * FROM raw_data;

OPTIMIZE sales_data
ZORDER BY (product_id);

This combination of partitioning and Z-Ordering maximizes query performance by leveraging the strengths of both techniques. Partitioning narrows down the data to relevant directories, while Z-Ordering optimizes data retrieval within those partitions.

Summary:

Partitioning: Great for columns like year, region, product_category, where range-based queries occur.
Z-Ordering: Ideal for columns like customer_id, product_id, or any frequently filtered/joined columns.

When used together, partitioning and Z-Ordering ensure that your queries read the least amount of data necessary, significantly improving performance for large datasets.

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Spark Must-Know Differences:

➤ RDD vs DataFrame:
- RDD: Low-level API, unstructured data, more control.
- DataFrame: High-level API, optimized, structured data.

➤ DataFrame vs Dataset:
- DataFrame: Untyped API, ease of use, suitable for Python.
- Dataset: Typed API, compile-time safety, best with Scala/Java.

➤ map() vs flatMap():
- map(): Transforms each element, returns a new RDD with the same number of elements.
- flatMap(): Transforms each element and flattens the result, can return a different number of elements.

➤ filter() vs where():
- filter(): Filters rows based on a condition, commonly used in RDDs.
- where(): SQL-like filtering, more intuitive in DataFrames.

➤ collect() vs take():
- collect(): Retrieves the entire dataset to the driver.
- take(): Retrieves a specified number of rows, safer for large datasets.

➤ cache() vs persist():
- cache(): Stores data in memory only.
- persist(): Stores data with a specified storage level (memory, disk, etc.).

➤ select() vs selectExpr():
- select(): Selects columns with standard column expressions.
- selectExpr(): Selects columns using SQL expressions.

➤ join() vs union():
- join(): Combines rows from different DataFrames based on keys.
- union(): Combines rows from DataFrames with the same schema.

➤ withColumn() vs withColumnRenamed():
- withColumn(): Creates or replaces a column.
- withColumnRenamed(): Renames an existing column.

➤ groupBy() vs agg():
- groupBy(): Groups rows by a column or columns.
- agg(): Performs aggregate functions on grouped data.

➤repartition() vs coalesce():
- repartition(): Increases or decreases the number of partitions, performs a full shuffle.
- coalesce(): Reduces the number of partitions without a full shuffle, more efficient for reducing partitions.

➤ orderBy() vs sort():
- orderBy(): Returns a new DataFrame sorted by specified columns, supports both ascending and descending.
- sort(): Alias for orderBy(), identical in functionality.

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10 Pyspark questions to clear your interviews.

1. How do you deploy PySpark applications in a production environment?
2. What are some best practices for monitoring and logging PySpark jobs?
3. How do you manage resources and scheduling in a PySpark application?
4. Write a PySpark job to perform a specific data processing task (e.g., filtering data, aggregating results).
5. You have a dataset containing user activity logs with missing values and inconsistent data types. Describe how you would clean and standardize this dataset using PySpark.
6. Given a dataset with nested JSON structures, how would you flatten it into a tabular format using PySpark?
8. Your PySpark job is running slower than expected due to data skew. Explain how you would identify and address this issue.
9. You need to join two large datasets, but the join operation is causing out-of-memory errors. What strategies would you use to optimize this join?
10. Describe how you would set up a real-time data pipeline using PySpark and Kafka to process streaming data

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Apache Airflow Interview Questions: Basic, Intermediate and Advanced Levels

𝗕𝗮𝘀𝗶𝗰 𝗟𝗲𝘃𝗲𝗹:

• What is Apache Airflow, and why is it used?
• Explain the concept of Directed Acyclic Graphs (DAGs) in Airflow.
• How do you define tasks in Airflow?
• What are the different types of operators in Airflow?
• How can you schedule a DAG in Airflow?

𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗟𝗲𝘃𝗲𝗹:

• How do you monitor and manage workflows in Airflow?
• Explain the difference between Airflow Sensors and Operators.
• What are XComs in Airflow, and how do you use them?
• How do you handle dependencies between tasks in a DAG?
• Explain the process of scaling Airflow for large-scale workflows.

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗟𝗲𝘃𝗲𝗹:

• How do you implement retry logic and error handling in Airflow tasks?
• Describe how you would set up and manage Airflow in a production environment.
• How can you customize and extend Airflow with plugins?
• Explain the process of dynamically generating DAGs in Airflow.
• Discuss best practices for optimizing Airflow performance and resource utilization.
• How do you manage and secure sensitive data within Airflow workflows?

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Data-engineer-handbook

This is a repo with links to everything you'd ever want to learn about data engineering

Creator: DataExpert-io
Stars ⭐️: 24.9k
Forked by: 4.9k

Github Repo:
https://github.com/DataExpert-io/data-engineer-handbook

#github