Top 10 Apache Spark Use Cases Across Industries and Their Impact in 2025

Apache Spark is revolutionizing how industries process and analyze massive datasets, powering applications like fraud detection in banking, real-time customer analytics in retail, predictive maintenance in manufacturing, and personalized recommendations in entertainment. 

Its in-memory processing and scalability make it a go-to tool for organizations seeking speed, accuracy, and efficiency in big data workflows.

In this blog, we explore the top 10 Apache Spark use cases transforming industries in 2025. From advanced machine learning to IoT data processing, you’ll discover how Spark is driving innovation, operational excellence, and competitive advantage across sectors.

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10 Practical Apache Spark Use Cases Across Industries

Apache Spark powers real-time analytics, deep learning models, and large-scale machine learning applications by leveraging in-memory computation and directed acyclic graph (DAG) execution. Industries such as finance, healthcare, and e-commerce depend on Spark for fraud detection, recommendation engines, and optimizing enterprise data pipelines.

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Let’s dive into 10 practical Apache Spark use cases across industries, complete with real-world examples, benefits, and actionable insights.

1. Data Processing & ETL  

Apache Spark processes large-scale data and ETL efficiently with in-memory computing, often outperforming Hadoop MapReduce depending on workload. Its RDD model minimizes data shuffling, optimizing performance. 

While prioritizing memory, Spark writes to disk when needed. It supports HDFS, S3, JDBC, and integrates with structured and unstructured data for real-time and batch ETL in enterprise pipelines.

Key features and how Spark can help: 

• Distributed computing for faster data processing.  
• Support for multiple data sources (e.g., HDFS, S3, databases).  
• Seamless integration with data warehouses like Snowflake and Redshift.  

Example code: 

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, IntegerType, StringType

# Create Spark session
spark = SparkSession.builder.appName("ETLExample").getOrCreate()

# Define schema explicitly (optional but recommended for performance & accuracy)
schema = StructType([
    StructField("name", StringType(), True),
    StructField("age", IntegerType(), True)
])

# Read CSV with schema and S3a format
df = spark.read.csv("s3a://data-bucket/raw-data.csv", header=True, schema=schema)

# Transform data: filter age > 30 and select relevant columns
transformed_df = df.filter(df["age"] > 30).select("name", "age")

# Write output to S3 in Parquet format
transformed_df.write.mode("overwrite").parquet("s3a://data-bucket/processed-data/")

Output:
name    age
---------------
Bob      35
Charlie  40
Eve      45

Industry example:  

• Netflix uses Spark for ETL pipelines to process petabytes of data daily, enabling personalized recommendations and operational efficiency. 

Also Read: PySpark Tutorial For Beginners [With Examples]

Bridging batch processing and real-time analytics, we move from Data Processing & ETL to the dynamic world of Stream Processing.

2. Stream Processing  

Stream processing handles continuous data streams in real time, enabling instant analytics and decision-making. Apache Spark Streaming processes unbounded data with micro-batches, ensuring low latency. 

It ingests data from sources like Kafka or HDFS, applies transformations, and outputs results dynamically, making it vital for fraud detection, stock trading, and personalized recommendations.

Key features and how Spark can help: 

• Micro-batch processing for near real-time analytics.  
• Integration with Kafka, Flume, and other streaming platforms.  
• Fault-tolerant and scalable architecture.  

Example code:  

from pyspark.sql import SparkSession
from pyspark.streaming import StreamingContext

# Create Spark Session and Context
spark = SparkSession.builder.appName("StreamingWordCount").getOrCreate()
sc = spark.sparkContext  # Get SparkContext from SparkSession

# Create Streaming Context with 10-second batch interval
ssc = StreamingContext(sc, batchDuration=10)

# Create a DStream that connects to a socket on localhost:9999
lines = ssc.socketTextStream("localhost", 9999)

# Process each line: Split into words
words = lines.flatMap(lambda line: line.split(" "))

# Map each word to (word, 1) and then reduce by key (word count)
word_counts = words.map(lambda word: (word, 1)).reduceByKey(lambda x, y: x + y)

# Print the results to console
word_counts.pprint()

# Start the streaming computation
ssc.start()

# Keep the application running until manually stopped
ssc.awaitTermination()

Output:
-------------------------------------------
Batch Time: 2024-02-04 12:00:10
-------------------------------------------
('hello', 3)
('world', 1)
('spark', 1)
('streaming', 1)

Industry example and case study:  

• Uber leverages Spark Streaming to process real-time trip data, optimizing routes and improving driver allocation.  

Also Read: Apache Spark Dataframes: Features, RDD & Comparison

With streaming data constantly feeding real-time insights, businesses can further enhance decision-making by applying machine learning models to extract deeper predictive intelligence.

3. Machine Learning & AI  

Spark’s MLlib offers distributed machine learning algorithms, optimizing tasks like classification, clustering, and recommendation at scale. It enables fast, in-memory computations across large datasets, reducing training time. 

MLlib integrates with TensorFlow and other AI frameworks. It supports feature extraction, model evaluation, and hyperparameter tuning, making it essential for real-time AI in big data environments.

Key features and how Spark can help:  

• Pre-built algorithms for classification, regression, clustering, and more.  
• Integration with TensorFlow and PyTorch for deep learning.  
• Distributed training for large datasets.  

Example code:  

from pyspark.sql import SparkSession
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.evaluation import BinaryClassificationEvaluator

# Create a Spark session
spark = SparkSession.builder.appName("SparkML_LogisticRegression").getOrCreate()

# Load dataset (example with CSV, assuming columns: 'feature1', 'feature2', ..., 'label')
data = spark.read.csv("s3a://your-bucket/dataset.csv", header=True, inferSchema=True)

# Assemble features into a single column
feature_cols = [col for col in data.columns if col != "label"]  # Exclude label column
vector_assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
data = vector_assembler.transform(data).select("features", "label")  # Keep only features & label

# Split data into training and test sets
(training_data, test_data) = data.randomSplit([0.8, 0.2], seed=42)

# Define Logistic Regression model
lr = LogisticRegression(featuresCol="features", labelCol="label")

# Train model
model = lr.fit(training_data)

# Make predictions on test data
predictions = model.transform(test_data)

# Evaluate model performance
evaluator = BinaryClassificationEvaluator(labelCol="label", metricName="areaUnderROC")
auc = evaluator.evaluate(predictions)

# Print evaluation result
print(f"Model AUC: {auc}")

# Stop Spark session
spark.stop()

Output:
Model AUC: 0.89

• AUC = 0.5 → Random guessing
• AUC = 0.7 - 0.8 → Decent performance
• AUC = 0.9+ → Very good model

AUC closer to 1 means better model performance in distinguishing between classes 0 and 1.

Industry example and case study:  

• LinkedIn uses Spark MLlib to power its "People You May Know" feature, improving user engagement.  

Also Read: Top 12 Spark Optimization Techniques

Leveraging advanced algorithms and intelligent systems to extract meaningful insights from data

4. Data Analytics  

Apache Spark processes massive datasets in-memory, accelerating data analytics. It supports distributed computing for structured and unstructured data. Businesses leverage Spark for real-time reporting, predictive modeling, and anomaly detection. 

Its MLlib library enables advanced machine learning, while SQL queries and graph analytics extract deep insights, driving data-driven decisions.

Key features and how Spark can help:  

• SQL-like queries with Spark SQL.  
• Interactive analytics with notebooks like Jupyter and Zeppelin.  
• Support for structured and semi-structured data.  

Example code: 

df.createOrReplaceTempView("sales")  

# Run SQL query to get total revenue per region
result = spark.sql("SELECT region, SUM(revenue) AS total_revenue FROM sales GROUP BY region")

# Show the result
result.show()

Input:

Output:
+--------+-------------+
| region | total_revenue |
+--------+-------------+
| North  | 3000              |
| South  | 3300              |
| East   | 1200                |
+--------+-------------+

Industry example and case study:  

• Walmart uses Spark for sales analytics, optimizing inventory management and pricing strategies.  

Also Read: Mapreduce in Big Data: Overview, Functionality & Importance

Bridging the gap between raw data insights and structured event tracking.

5. Log Processing  

Log files capture system events, errors, and user activities, making them crucial for monitoring and troubleshooting. Apache Spark efficiently processes massive logs from distributed storage (HDFS, S3, etc.), filtering relevant data like errors, warnings, or security threats in real-time or batch mode. It enables scalable, fast, and structured log analysis for operational insights.  

Key features and how Spark can help:  

• Real-time log aggregation and analysis.  
• Anomaly detection for security and performance monitoring.  
• Integration with ELK Stack (Elasticsearch, Logstash, Kibana).  

Example code:

from pyspark.sql import SparkSession

# Create a Spark session
spark = SparkSession.builder.appName("LogProcessing").getOrCreate()

# Read log file from HDFS
logs = spark.read.text("hdfs://logs/access.log")

# Filter out lines containing "ERROR"
error_logs = logs.filter(logs["value"].contains("ERROR"))

# Write error logs to HDFS in text format (better than CSV for logs)
error_logs.write.mode("overwrite").text("hdfs://logs/errors/")

Output:

• Initializes a SparkSession named "LogProcessing", which is required to use Spark SQL and DataFrame operations.
• Loads a log file (access.log) stored in HDFS as a text-based DataFrame where each line is treated as a row under the "value" column.
• Uses the .filter() function to extract only lines that contain the word "ERROR", effectively isolating error logs.
• Saves the extracted error logs in HDFS under the "hdfs://logs/errors/" directory in text format, using .mode("overwrite") to replace previous data.  

Industry example and case study:  

• Cloudflare uses Spark to analyze vast amounts of traffic logs, identifying and mitigating DDoS attacks before they impact clients.

Also Read: Flink Vs. Spark: Difference Between Flink and Spark 

Building on efficient log processing, the emergence of fog computing enables decentralized data processing closer to the source, reducing latency and enhancing real-time insights.

6. Fog Computing  

Fog computing decentralizes data processing by handling computations at the network edge, reducing latency and bandwidth usage. Spark’s distributed processing, in-memory computation, and micro-batch streaming enable real-time analytics on IoT-generated data at fog nodes. 

Its scalability and resilience make it suitable for processing sensor data, anomaly detection, and localized machine learning near edge devices.

Key features and how Spark can help:  

• Edge analytics for IoT devices.  
• Reduced latency and bandwidth usage.  
• Scalable and fault-tolerant architecture.  

Example code: 

from pyspark.sql import SparkSession

# Create Spark session
spark = SparkSession.builder.appName("FogComputingEdgeProcessing").getOrCreate()

# Load Edge Device Data (assuming local or cloud storage)
edge_data = spark.read.json("edge-device-data.json")

# Filter high-temperature readings (above 30 degrees)
processed_data = edge_data.filter(edge_data["temperature"] > 30)

# Write output to a directory in JSON format
processed_data.write.mode("overwrite").json("processed-edge-data/")

Code Explanation:

• The script loads IoT sensor data from a JSON file (edge-device-data.json), which could be stored locally, in S3, or in HDFS.
• It selects only records where the "temperature" value is greater than 30, simulating edge-level processing.
• The filtered results are saved in a new JSON file (processed-edge-data/), ensuring efficient storage for further analysis.

Industry example and case study:  

• Schneider Electric leverages Spark in industrial automation, using fog computing to monitor factory sensors in real time, detecting anomalies before they cause equipment failures.

Also Read: Explore the Top 10 Big Data Tools for Businesses

Bridging the gap between decentralized data processing and intelligent decision-making, the integration of fog computing with recommendation systems enhances efficiency and personalization. 

7. Recommendation Systems  

Spark enables scalable, data-driven recommendation systems by leveraging collaborative filtering (ALS), content-based filtering, and hybrid models. It efficiently processes massive datasets in real-time, identifying user preferences and behavioral patterns. 

Businesses use Spark for personalized product, movie, or content recommendations, optimizing engagement and retention. Its distributed computing model ensures fast, adaptive, and accurate predictions across industries.

Key features and how Spark can help:  

• Collaborative filtering algorithms.  
• Real-time recommendations with Spark Streaming.  
• Scalable for millions of users and products.  

Example code: 

from pyspark.sql import SparkSession
from pyspark.ml.recommendation import ALS

# Step 1: Initialize Spark Session
spark = SparkSession.builder.appName("ALSRecommendationSystem").getOrCreate()

# Step 2: Sample Data (Replace with real dataset)
ratings_data = spark.createDataFrame([
    (0, 1, 4.0),
    (0, 3, 2.0),
    (1, 2, 5.0),
    (1, 3, 3.0),
    (2, 1, 4.5),
    (2, 2, 3.5),
], ["userId", "movieId", "rating"])

# Step 3: Train ALS Model
als = ALS(
    userCol="userId",
    itemCol="movieId",
    ratingCol="rating",
    rank=10,
    maxIter=5,
    regParam=0.01,
    coldStartStrategy="drop"  # Important to avoid NaN predictions
)

model = als.fit(ratings_data)

# Step 4: Generate Top 10 Recommendations for Each User
recommendations = model.recommendForAllUsers(10)

# Show Recommendations
recommendations.show(truncate=False)

Output:
+------+--------------------------------------------------+
|userId|recommendations                                                      |
+------+--------------------------------------------------+
|  0   |[{2, 4.8}, {1, 3.9}, {3, 3.5}]                                             |
|  1   |[{1, 4.7}, {3, 4.1}, {2, 3.8}]                                              |
|  2   |[{3, 4.9}, {1, 4.3}, {2, 3.6}]                                            |
+------+--------------------------------------------------+

Industry example and case study:  

• Spotify uses Spark to recommend songs and playlists, driving user engagement.  

Also Read: Data Analysis Using Python

Recommendation systems personalize user experiences by predicting preferences, while real-time advertising dynamically delivers targeted ads based on instant data insights.

8. Real-time Advertising  

Apache Spark processes massive ad auction data streams in milliseconds, enabling real-time bidding (RTB) where advertisers compete for impressions. It analyzes user behavior, demographics, and engagement patterns, optimizing ad placement dynamically. 

By integrating machine learning models, Spark predicts click-through rates (CTR) and conversion probabilities, ensuring precise targeting and higher ad relevance, maximizing advertisers’ ROI.

Key features and how Spark can help:  

• Real-time analytics for ad impressions and clicks.  
• Personalized ad targeting using machine learning.  
• Integration with ad exchanges and DSPs.  

Example code: 

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

# Create Spark Session
spark = SparkSession.builder.appName("RealTimeAdClickProcessing").getOrCreate()

# Read streaming data from a JSON source (e.g., Kafka, socket, or directory)
ad_clicks = spark.readStream.format("json").option("path", "ad-clicks-stream/").load()

# Filter high-value clicks (revenue > 10)
high_value_clicks = ad_clicks.filter(col("revenue") > 10)

# Write output to a JSON sink in append mode (or Kafka, database, etc.)
query = high_value_clicks.writeStream \
    .format("json") \
    .option("path", "high-value-clicks/") \
    .option("checkpointLocation", "checkpoint/") \
    .outputMode("append") \
    .start()

# Keep the streaming job running
query.awaitTermination()

Code Explanation:

• A real-time stream of ad clicks comes from Kafka, Flume, or a web server.
• Spark processes each click as it arrives.
• If the ad generated revenue > $10, it's saved for further analysis.
• This helps optimize ad spend, detect fraud, and improve ad targeting.

Industry example and case study:  

• The Trade Desk uses Spark for real-time ad analytics, optimizing ad spend for clients.  

Also Read: Top Big Data Skills Employers Are Looking For in 2025!

Enhancing marketing strategies with instant insights, real-time advertising leverages financial data analysis to optimize decision-making and maximize ROI effectively.

9. Financial Data Analysis  

Spark processes massive financial datasets swiftly, identifying anomalies for fraud detection, calculating real-time risk metrics, and optimizing algorithmic trading strategies by analyzing market trends. 

Its distributed computing power handles high-frequency trading data and complex simulations, providing faster insights and data-driven decisions essential for financial markets’ volatility and regulatory demands. 

Key features and how Spark can help:  

• Real-time fraud detection with streaming data.  
• Risk modeling with machine learning.  
• Scalable for high-frequency trading.  

Example code:

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, DoubleType, StringType

# Step 1: Create Spark Session
spark = SparkSession.builder.appName("FinancialFraudDetection").getOrCreate()

# Step 2: Define Schema (Explicitly Casting 'amount' as Double)
schema = StructType([
    StructField("transaction_id", StringType(), True),
    StructField("customer_id", StringType(), True),
    StructField("amount", DoubleType(), True),  # Cast amount to DoubleType
    StructField("timestamp", StringType(), True)
])

# Step 3: Read CSV with Schema
fraud_data = spark.read.csv("transactions.csv", header=True, schema=schema)

# Step 4: Filter Transactions Where Amount > 10,000
fraudulent_transactions = fraud_data.filter(fraud_data["amount"] > 10000)

# Step 5: Save Fraudulent Transactions to a CSV File
fraudulent_transactions.write.mode("overwrite").csv("fraud-alerts/")  

Explanation of Code:

• The program reads financial transaction data from a CSV file, ensuring the amount column is correctly cast as DoubleType for accurate filtering.
• Filters fraudulent transactions where the amount exceeds $10,000 and stores the results in a new CSV file (fraud-alerts/).

Industry example and case study:  

• JPMorgan Chase uses Spark for fraud detection, saving millions annually. 

Also Read: Data Science Vs Data Analytics: Difference Between Data Science and Data Analytics

Financial analytics ensures security, while real-time Spark processing aids IoT by detecting anomalies, predicting failures, and optimizing operations. 

10. Internet of Things (IoT) Analytics  

Apache Spark processes real-time IoT data for anomaly detection, predictive maintenance, and insights. It optimizes traffic, monitors vitals, and prevents equipment failures efficiently. In smart cities, it optimizes traffic flow; in healthcare, it monitors patient vitals through proactive analytics.

Key features and how Spark can help:  

• Real-time monitoring and analytics.  
• Predictive maintenance for IoT devices.  
• Integration with IoT platforms like AWS IoT and Azure IoT.  

Example code: 

from pyspark.sql import SparkSession

# Initialize Spark Session
spark = SparkSession.builder.appName("IoTAnalytics").getOrCreate()

# Read JSON file containing IoT sensor data
iot_data = spark.read.json("iot-sensor-data.json")

# Filter anomalies where temperature > 100
anomalies = iot_data.filter(iot_data["temperature"] > 100)

# Save anomalies to JSON (overwrite mode to prevent errors)
anomalies.write.mode("overwrite").json("iot-anomalies/") 

Explanation of Code:

• Only records where temperature > 100 are saved.
• Multiple partitioned files (part-xxxxx.json) are generated because Spark runs in a distributed environment.
• _SUCCESS file is created in iot-anomalies/, indicating successful execution.

Industry example and case study  

• Siemens uses Spark for IoT analytics in smart factories, improving operational efficiency.  

Also Read: Top 10 IoT Real-World Applications in 2025 You Should Be Aware Of

While Apache Spark offers powerful solutions across various industries, leveraging its capabilities comes with its own set of challenges and advantages.

Challenges and Benefits of Apache Spark Applications  

Apache Spark is a game-changer in the world of big data, offering unparalleled speed and scalability. However, like any technology, it comes with its own set of challenges. Whether you're a student learning about big data or a professional exploring Apache Spark use cases, understanding its benefits and limitations is crucial.  

No innovation is flawless. While Spark can transform how you process and analyze data, it’s important to consider its hurdles. Let’s break down the key benefits and challenges of Apache Spark applications.  

Benefits of Apache Spark  

Apache Spark is a powerful tool that can revolutionize your data workflows. Here’s why it’s widely adopted across industries:  

1. Speed of Execution  

Spark’s in-memory processing can make it significantly faster than traditional tools like Hadoop MapReduce, with potential speed improvements of up to 100x for certain workloads. However, actual performance gains depend on workload characteristics, and Spark may still write to disk when memory is insufficient."  

2. Multi-language Support  

Write code in Python, Scala, Java, or R, depending on your preference or project requirements.  

3. Unified Analytics Engine  

Combine batch processing, streaming, machine learning, and graph processing in a single platform.  

4. Scalability  

Handle petabytes of data with ease, making it ideal for large-scale applications. 

Now, let’s explore the possible challenges of apache spark.

Challenges of Apache Spark  

While Spark offers numerous benefits, its implementation across industries comes with specific hurdles. Understanding these challenges can help organizations make informed decisions.

1. File System Dependency

Spark relies on external storage systems like HDFS, S3, or databases, which can introduce latency. 

Industry Impact: In healthcare, where patient records are often stored across multiple systems, integrating Spark with legacy databases can slow down analytics workflows. 

Example: If your data is stored in a slow file system, Spark’s performance may suffer.  

2. Micro-Batch Processing  

Spark Streaming uses micro-batch processing, which may not be truly real-time for some applications.  

Industry Impact: In finance, where milliseconds matter for fraud detection, Spark's slight delay may be a disadvantage.

Example: For ultra-low-latency use cases, alternatives like Apache Flink might be more suitable.  

3. Cost Implications  

Spark’s in-memory processing requires significant RAM, which can increase infrastructure costs.  

Industry Impact: In retail, where demand forecasting requires large-scale data processing, companies may struggle with budget constraints.

Example: A large e-commerce company may find that scaling Spark clusters for holiday traffic predictions leads to unexpected cloud costs. 

4. Handling Numerous Small Files  

Spark performs poorly with small files, as it’s optimized for large datasets.  

Industry Impact: In media and entertainment, where logs and metadata from streaming services generate thousands of small files, performance bottlenecks may arise.

Example: If you’re processing thousands of small log files, you may need to preprocess them into larger chunks.  

5. Limited Out-of-the-Box ML Algorithms  

While Spark MLlib offers many algorithms, it may not cover all advanced machine learning needs.  

Industry Impact: In pharmaceuticals, where drug discovery relies on complex deep learning models, Spark MLlib alone may not suffice.

Example: For deep learning, you might need to integrate Spark with TensorFlow or PyTorch.  

6. Manual Code Optimization  

Achieving optimal performance often requires fine-tuning Spark configurations and code.  

Industry Impact: In pharmaceuticals, where drug discovery relies on complex deep learning models, Spark MLlib alone may not suffice.

Example: Adjusting parameters like `spark.executor.memory` and `spark.sql.shuffle.partitions` can be time-consuming.   

Also Read: Top 10 Major Challenges of Big Data & Simple Solutions To Solve Them

Mastering the challenges and advantages of Apache Spark applications can elevate your career, showcasing expertise in this powerful framework.

Why Learning Apache Spark is a Smart Career Move?  

As industries like finance, healthcare, e-commerce, and manufacturing increasingly rely on big data, mastering Apache Spark can give you a competitive edge. Whether you're a student entering the tech world or a professional upskilling for data-driven roles, understanding Spark’s applications in these sectors can open doors to valuable opportunities. Let’s explore how Spark powers innovation across industries.

1. Unified Analytics Engine  

Apache Spark’s ability to handle batch processing, streaming, machine learning, and graph analytics in one platform makes it indispensable. You won’t need to juggle multiple tools, which saves time and reduces complexity.   

2. Multi-language Support  

Spark’s flexibility lets you code in Python, Scala, Java, or R. This means you can leverage your existing programming skills while learning Spark.  

3. Future-proof Skillset  

Spark runs on AWS, Azure, and Google Cloud, aligning with cloud-first strategies. Its MLlib integrates with TensorFlow and PyTorch, making it critical for AI roles. 

4. Business Impact  

Companies adopt Spark to drive revenue, cut costs, and innovate. Understanding Apache Spark use cases across industries lets you translate technical skills into real-world impact.   

Also Read: Big Data Technology: Transforming Data into Actionable Insights

Gaining expertise in Apache Spark opens doors to exciting career opportunities—discover how upGrad can equip you with essential skills.

Conclusion

Apache Spark use cases demonstrate its transformative impact across industries, from real-time analytics and ETL to machine learning, IoT, and financial data analysis. 

By using Spark’s in-memory computation, distributed processing, and integration with multiple programming languages and cloud platforms, businesses can process massive datasets efficiently, gain actionable insights, and drive innovation. 

Understanding these Apache Spark use cases equips professionals with the skills to tackle complex data challenges and positions them for rewarding careers in big data and analytics.

How upGrad Can Help You Learn Apache Spark?  

upGrad offers specialized programs to help professionals master Spark optimization and big data technologies through hands-on learning and expert mentorship, equipping you with industry-relevant skills to advance in the fast-evolving big data landscape.

Here are some recommended courses you can check out:

• Java Object-oriented Programming
• Programming with Python: Introduction for Beginners
• Data Structures and Algorithms Certification courses
• JavaScript Basics from Scratch
• Advanced SQL: Functions and Formulas

Also, get personalized career counseling with upGrad to shape your programming future, or you can visit your nearest upGrad center and start hands-on training today!

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