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SNOWFLAKES AND DATABRICKS

Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs?

🌐 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞

❄️ 𝐍𝐚𝐭𝐮𝐫𝐞: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup.

❄️ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading.
❄️ Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility.

❄️ 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads.

❄️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools.

🌐 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬

❄️ 𝐂𝐨𝐫𝐞: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently.

❄️ 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework.

🌐 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬

❄️ 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐍𝐞𝐞𝐝𝐬: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements.

❄️ 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞’𝐬 𝐈𝐝𝐞𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing.

❄️ 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞𝐬: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricks—with its schema-on-read technique—may be more advantageous.

🌐 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:

Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy.

Roadmap to crack product-based companies for Big Data Engineer role:

1. Master Python, Scala/Java
2. Ace Apache Spark, Hadoop ecosystem
3. Learn data storage (SQL, NoSQL), warehousing
4. Expertise in data streaming (Kafka, Flink/Storm)
5. Master workflow management (Airflow)
6. Cloud skills (AWS, Azure or GCP)
7. Data modeling, ETL/ELT processes
8. Data viz tools (Tableau, Power BI)
9. Problem-solving, communication, attention to detail
10. Projects, certifications (AWS, Azure, GCP)
11. Practice coding, system design interviews

Here, you can find Data Engineering Resources 👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

Most asked Python interview questions for Data Engineer jobs with answers!

𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗹𝗶𝘀𝘁𝘀 𝗮𝗻𝗱 𝘁𝘂𝗽𝗹𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻.
Lists are mutable, meaning their elements can be changed but Tuples are immutable.

𝟮. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲 𝗶𝗻 𝗽𝗮𝗻𝗱𝗮𝘀?
A DataFrame is a 2-dimensional labelled data structure, similar to a spreadsheet.

𝟯. 𝗥𝗲𝘃𝗲𝗿𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗼𝗿𝗱𝘀 𝗶𝗻 𝗮 𝘀𝘁𝗿𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻
def reverse_words(s: str) -> str:
words = s.split()
reversed_words = reversed(words)
return ' '.join(reversed_words)

𝟰. 𝗪𝗿𝗶𝘁𝗲 𝗮 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗰𝗼𝘂𝗻𝘁 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝘃𝗼𝘄𝗲𝗹𝘀 𝗶𝗻 𝗮 𝗴𝗶𝘃𝗲𝗻 𝘀𝘁𝗿𝗶𝗻𝗴?
def count_vowels(string: str) -> int:
vowels = "aeiouAEIOU"
vowel_count = 0
for char in string:
if char in vowels:
vowel_count += 1
return vowel_count

I’ve listed 4 but there are many questions you’d need to prepare to succeed in interviews.

Here, you can find Data Engineering Interview Resources 👇 https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

Here are top 40 commonly asked pyspark questions that you can prepare for interviews.

𝗥𝗗𝗗𝘀 -
1. What is an RDD in Apache Spark? Explain its characteristics.
2. How are RDDs fault-tolerant in Apache Spark?
3. What are the different ways to create RDDs in Spark?
4. Explain the difference between transformations and actions in RDDs.
5. How does Spark handle data partitioning in RDDs?
6. Can you explain the lineage graph in RDDs and its significance?
7. What is lazy evaluation in Apache Spark RDDs?
8. How can you persist RDDs in memory for faster access?
9. Explain the concept of narrow and wide transformations in RDDs.
10. What are the limitations of RDDs compared to DataFrames and Datasets?

𝗗𝗮𝘁𝗮𝗳𝗿𝗮𝗺𝗲 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀 -
1. What are DataFrames and Datasets in Apache Spark?
2. What are the differences between DataFrame and RDD?
3. Explain the concept of a schema in a DataFrame.
4. How are DataFrames and Datasets fault-tolerant in Spark?
5. What are the advantages of using DataFrames over RDDs?
6. Explain the Catalyst optimizer in Apache Spark.
7. How can you create DataFrames in Apache Spark?
8. What is the significance of Encoders in Datasets?
9. How does Spark SQL optimize the execution plan for DataFrames?
10. Can you explain the benefits of using Datasets over DataFrames?

𝗦𝗽𝗮𝗿𝗸 𝗦𝗤𝗟 -
1. What is Spark SQL, and how does it relate to Apache Spark?
2. How does Spark SQL leverage DataFrame and Dataset APIs?
3. Explain the role of the Catalyst optimizer in Spark SQL.
4. How can you run SQL queries on DataFrames in Spark SQL?
5. What are the benefits of using Spark SQL over traditional SQL queries?

𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 -
1. What are some common performance bottlenecks in Apache Spark applications?
2. How can you optimize the shuffle operations in Spark?
3. Explain the significance of data skew and techniques to handle it in Spark.
4. What are some techniques to optimize Spark job execution time?
5. How can you tune memory configurations for better performance in Spark?
6. What is dynamic allocation, and how does it optimize resource usage in Spark?
7. How can you optimize joins in Spark?
8. What are the benefits of partitioning data in Spark?
9. How does Spark leverage data locality for optimization?

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

5 most asked SQL Interview Questions for Data Engineer jobs.

𝟭. 𝗙𝗶𝗻𝗱 𝘁𝗵𝗲 𝗦𝗲𝗰𝗼𝗻𝗱 𝗛𝗶𝗴𝗵𝗲𝘀𝘁 𝗦𝗮𝗹𝗮𝗿𝘆 𝗶𝗻 𝗮 𝗧𝗮𝗯𝗹𝗲

SELECT MAX(salary) AS SecondHighestSalary
FROM Employee
WHERE salary < (SELECT MAX(salary) FROM Employee);

𝟮 . 𝗙𝗶𝗻𝗱 𝗼𝘂𝘁 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘁𝗵𝗲𝗶𝗿 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀

SELECT e2.name as Employee
FROM employee e1
INNER JOIN employee e2
ON e1.id = e2.managerID
WHERE e1.salary < e2.salary

𝟯. 𝗙𝗶𝗻𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝘄𝗵𝗼 𝗻𝗲𝘃𝗲𝗿 𝗼𝗿𝗱𝗲𝗿

SELECT name as Customers
FROM Customers
WHERE id not in (
SELECT customerId
FROM Orders);

𝟰. 𝗗𝗲𝗹𝗲𝘁𝗲 𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗲𝗺𝗮𝗶𝗹𝘀

DELETE p1
FROM Person p1, Person p2
WHERE p1.Email = p2.Email AND
p1.Id > p2.Id

𝟱. 𝗖𝗼𝘂𝗻𝘁 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗼𝗿𝗱𝗲𝗿𝘀 𝗽𝗹𝗮𝗰𝗲𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗽𝗿𝗲𝘃𝗶𝗼𝘂𝘀 𝘆𝗲𝗮𝗿 𝗮𝗻𝗱 𝗺𝗼𝗻𝘁𝗵.

SELECT COUNT(*) AS order_count
FROM orders WHERE EXTRACT(YEAR_MONTH FROM order_date) = EXTRACT(YEAR_MONTH FROM CURDATE() - INTERVAL 1 MONTH);

💡 Note: SQL interview questions vary widely based on the specific role and company. So you also need to practice questions your target companies ask.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍

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Thinking about becoming a Data Engineer? Here's the roadmap to avoid pitfalls & master the essential skills for a successful career.

📊Introduction to Data Engineering

✅Overview of Data Engineering & its importance
✅Key responsibilities & skills of a Data Engineer
✅Difference between Data Engineer, Data Scientist & Data Analyst
✅Data Engineering tools & technologies

📊Programming for Data Engineering

✅Python
✅SQL
✅Java/Scala
✅Shell noscripting

📊Database System & Data Modeling

✅Relational Databases: design, normalization & indexing
✅NoSQL Databases: key-value stores, document stores, column-family stores & graph database
✅Data Modeling: conceptual, logical & physical data model
✅Database Management Systems & their administration

📊Data Warehousing and ETL Processes

✅Data Warehousing concepts: OLAP vs. OLTP, star schema & snowflake schema
✅ETL: designing, developing & managing ETL processe
✅Tools & technologies: Apache Airflow, Talend, Informatica, AWS Glue
✅Data lakes & modern data warehousing solution

📊Big Data Technologies

✅Hadoop ecosystem: HDFS, MapReduce, YARN
✅Apache Spark: core concepts, RDDs, DataFrames & SparkSQL
✅Kafka and real-time data processing
✅Data storage solutions: HBase, Cassandra, Amazon S3

📊Cloud Platforms & Services

✅Introduction to cloud platforms: AWS, Google Cloud Platform, Microsoft Azure
✅Cloud data services: Amazon Redshift, Google BigQuery, Azure Data Lake
✅Data storage & management on the cloud
✅Serverless computing & its applications in data engineering

📊Data Pipeline Orchestration

✅Workflow orchestration: Apache Airflow, Luigi, Prefect
✅Building & scheduling data pipelines
✅Monitoring & troubleshooting data pipelines
✅Ensuring data quality & consistency

📊Data Integration & API Development

✅Data integration techniques & best practices
✅API development: RESTful APIs, GraphQL
✅Tools for API development: Flask, FastAPI, Django
✅Consuming APIs & data from external sources

📊Data Governance & Security

✅Data governance frameworks & policies
✅Data security best practices
✅Compliance with data protection regulations
✅Implementing data auditing & lineage

📊Performance Optimization & Troubleshooting

✅Query optimization techniques
✅Database tuning & indexing
✅Managing & scaling data infrastructure
✅Troubleshooting common data engineering issues

📊Project Management & Collaboration

✅Agile methodologies & best practices
✅Version control systems: Git & GitHub
✅Collaboration tools: Jira, Confluence, Slack
✅Documentation & reporting

Resources for Data Engineering
1️⃣Python: https://news.1rj.ru/str/pythonanalyst

2️⃣SQL: https://news.1rj.ru/str/sqlanalyst

3️⃣Excel: https://news.1rj.ru/str/excel_analyst

4️⃣Free DE Courses: https://news.1rj.ru/str/free4unow_backup/569

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

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Complete topics & subtopics of #SQL for Data Engineer role:-

𝟭. 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗦𝘆𝗻𝘁𝗮𝘅:
SQL keywords
Data types
Operators
SQL statements (SELECT, INSERT, UPDATE, DELETE)

𝟮. 𝗗𝗮𝘁𝗮 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗗𝗟):
CREATE TABLE
ALTER TABLE
DROP TABLE
Truncate table

𝟯. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗠𝗟):
SELECT statement (SELECT, FROM, WHERE, ORDER BY, GROUP BY, HAVING, JOINs)
INSERT statement
UPDATE statement
DELETE statement

𝟰. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
SUM, AVG, COUNT, MIN, MAX
GROUP BY clause
HAVING clause

𝟱. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀:
Primary Key
Foreign Key
Unique
NOT NULL
CHECK

𝟲. 𝗝𝗼𝗶𝗻𝘀:
INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN
Self Join
Cross Join

𝟳. 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀:
Types of subqueries (scalar, column, row, table)
Nested subqueries
Correlated subqueries

𝟴. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
String functions (CONCAT, LENGTH, SUBSTRING, REPLACE, UPPER, LOWER)
Date and time functions (DATE, TIME, TIMESTAMP, DATEPART, DATEADD)
Numeric functions (ROUND, CEILING, FLOOR, ABS, MOD)
Conditional functions (CASE, COALESCE, NULLIF)

𝟵. 𝗩𝗶𝗲𝘄𝘀:
Creating views
Modifying views
Dropping views

𝟭𝟬. 𝗜𝗻𝗱𝗲𝘅𝗲𝘀:
Creating indexes
Using indexes for query optimization

𝟭𝟭. 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀:
ACID properties
Transaction management (BEGIN, COMMIT, ROLLBACK, SAVEPOINT)
Transaction isolation levels

𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:
Data integrity constraints (referential integrity, entity integrity)
GRANT and REVOKE statements (granting and revoking permissions)
Database security best practices

𝟭𝟯. 𝗦𝘁𝗼𝗿𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
Creating stored procedures
Executing stored procedures
Creating functions
Using functions in queries

𝟭𝟰. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻:
Query optimization techniques (using indexes, optimizing joins, reducing subqueries)
Performance tuning best practices

𝟭𝟱. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Recursive queries
Pivot and unpivot operations
Window functions (Row_number, rank, dense_rank, lead & lag)
CTEs (Common Table Expressions)
Dynamic SQL

Here you can find quick SQL Revision Notes👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

Like for more

Hope it helps :)

𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗔𝗪𝗦, 𝗜𝗕𝗠, 𝗖𝗶𝘀𝗰𝗼, 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱. 😍

- Python
- Artificial Intelligence,
- Cybersecurity
- Cloud Computing, and
- Machine Learning

𝐋𝐢𝐧𝐤 👇:-

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https://clnk.in/soLY

Data Engineering Free Books

https://ia600201.us.archive.org/4/items/springer_10.1007-978-1-4419-0176-7/10.1007-978-1-4419-0176-7.pdf

https://www.darwinpricing.com/training/Data_Engineering_Cookbook.pdf

Big Data of Data Engineering Free book

https://databricks.com/wp-content/uploads/2021/10/Big-Book-of-Data-Engineering-Final.pdf

https://aimlcommunity.com/wp-content/uploads/2019/09/Data-Engineering.pdf

The Data Engineer’s Guide to Apache Spark

https://news.1rj.ru/str/datasciencefun/783

Data Engineering with Python

https://news.1rj.ru/str/pythondevelopersindia/343

Data Engineering Projects -

1.End-To-End From Web Scraping to Tableau  https://lnkd.in/ePMw63ge

2. Building Data Model and Writing ETL Job https://lnkd.in/eq-e3_3J

3. Data Modeling and Analysis using Semantic Web Technologies https://lnkd.in/e4A86Ypq

4. ETL Project in Azure Data Factory - https://lnkd.in/eP8huQW3

5. ETL Pipeline on AWS Cloud - https://lnkd.in/ebgNtNRR

6. Covid Data Analysis Project - https://lnkd.in/eWZ3JfKD

7. YouTube Data Analysis 
   (End-To-End Data Engineering Project) - https://lnkd.in/eYJTEKwF

8. Twitter Data Pipeline using Airflow - https://lnkd.in/eNxHHZbY

9. Sentiment analysis Twitter:
    Kafka and Spark Structured Streaming -  https://lnkd.in/esVAaqtU

ENJOY LEARNING 👍👍

𝟯 𝗙𝗥𝗘𝗘 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟱😍

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Planning for Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.


𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

Discussion Group: https://news.1rj.ru/str/freshersgrp

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿?

Here is a complete week-by-week roadmap that can help

𝗪𝗲𝗲𝗸 𝟭: Learn programming - Python for data manipulation, and Java for big data frameworks.

𝗪𝗲𝗲𝗸 𝟮-𝟯: Understand database concepts and databases like MongoDB.

𝗪𝗲𝗲𝗸 𝟰-𝟲: Start with data warehousing (ETL), Big Data (Hadoop) and Data pipelines (Apache AirFlow)

𝗪𝗲𝗲𝗸 𝟲-𝟴: Go for advanced topics like cloud computing and containerization (Docker).

𝗪𝗲𝗲𝗸 𝟵-𝟭𝟬: Participate in Kaggle competitions, build projects and develop communication skills.

𝗪𝗲𝗲𝗸 𝟭𝟭: Create your resume, optimize your profiles on job portals, seek referrals and apply.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

𝟰 𝗙𝗥𝗘𝗘 𝗕𝗲𝘀𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝗮𝘃𝗮 𝗘𝗮𝘀𝗶𝗹𝘆 😍

Level up your Java skills without getting overwhelmed

All of them are absolutely free, designed by experienced educators and top tech creators

𝐋𝐢𝐧𝐤 👇:-

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Enroll For FREE & Get Certified 🎓

Complete Data Engineering Roadmap to keep yourself in the hunt in job market.

1. I will Learn SQL
--variables, data types, Aggregate functions
-- Various joins, data analysis
-- data wrangling, operators like(union, intersect etc.)
--Advanced SQL(Regex, Having, PIVOT)
--Windowing functions, CTE
--finally performance optimizations.

2. I will learn Python...
-- Basic functions, constructors, Lists, Tuples, Dictionaries
-- Loops (IF, When, FOR), functional programming
-- Libraries like(Pandas, Numpy, scikit-learn etc)

3. Learn distributed computing...
--Hadoop versions/hadoop architecture
--fault tolerance in hadoop
--Read/understand about Mapreduce processing.
--learn optimizations used in mapreduce etc.

4. Learn data ingestion tools...
--Learn Sqoop/ Kafka/NIFi
--Understand their functionality and job running mechanism.

5. i ll Learn data processing/NOSQL....
--Spark architecture/ RDD/Dataframes/datasets.
--lazy evaluation, DAGs/ Lineage graph/optimization techniques
--YARN utilization/ spark streaming etc.

6. Learn data warehousing.....
--Understand how HIve store and process the data
--different File formats/ compression Techniques.
--partitioning/ Bucketing.
--different UDF's available in Hive.
--SCD concepts.
--Ex Hbase. cassandra

7. Learn job Orchestration...
--Learn Airflow/Oozie
--learn about workflow/ CRON etc.

8. Learn Cloud Computing....
--Learn Azure/AWS/ GCP.
--understand the significance of Cloud in #dataengineering
--Learn Azure synapse/Redshift/Big query
--Learn Ingestion tools/pipeline tools like ADF etc.

9. Learn basics of CI/ CD and Linux commands....
--Read about Kubernetes/Docker. And how crucial they are in data.
--Learn about basic commands like copy data/export in Linux.

Data Engineering Interview Preparation Resources: 👇 https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

Like if you need similar content 😄👍

Hope this helps you 😊

𝟯 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to build your tech career without breaking the bank?💰

These 3 completely free courses are all you need to begin your journey in programming and data analysis📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3EtHnBI

Learn at your own pace, sharpen your skills, and showcase your progress on LinkedIn or your resume. Let’s dive in!✅️

10 Data Engineering Projects to build your portfolio.

1. Olympic Data Analytics using Azure
https://lnkd.in/gHNyz_Bg

2. Uber Data Analytics using GCP.
https://lnkd.in/gqE-Y4HS

3. Stock Market Real-time Data Analysis using Kafka
https://lnkd.in/gknh7ZEr

4. Twitter Data Pipeline using Airflow
https://lnkd.in/g7YPnH7G

5. Smart City End to End project using AWS
https://lnkd.in/gh2eWF66

6. Realtime Data Streaming using spark and Kafka
https://lnkd.in/gjH2efgz

7. Zillow Data Analytics - Python, ETL
https://lnkd.in/gvEVZHPR

8. End to end Azure Project
https://lnkd.in/gCVZtNB5

9. End to end project using snowlake
https://lnkd.in/g96n6NbA

10. Data pipeline using Data Fusion
https://lnkd.in/gR5pkeRw

Data Engineering Interview Preparation Resources: 👇 https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

Hope this helps you 😊

If you've read so far, do LIKE the post👍