In today's data-driven world, organizations are faced with the challenge of efficiently processing and analyzing vast amounts of data to extract valuable insights. Apache Spark has emerged as a powerful tool for processing big data, offering speed, scalability, and ease of use. This project aims to leverage the capabilities of Spark to enhance data processing efficiency and empower organizations to derive meaningful insights from their data.Scalable Data Processing: Implement Spark to process large-scale datasets in a distributed computing environment, enabling parallel processing for enhanced scalability. Real-time Data Analytics: Utilize Spark Streaming to perform real-time analytics on streaming data sources, enabling organizations to make timely decisions based on up-to-date information. Advanced Analytics: Employ Spark's machine learning library (MLlib) to perform advanced analytics tasks such as predictive modeling, clustering, and classification, enabling organizations to uncover patterns and trends within their data. Integration with Big Data Ecosystem: Integrate Spark seamlessly with other components of the big data ecosystem such as Hadoop, Kafka, and Cassandra, enabling seamless data ingestion, storage, and processing across different platforms. Optimization and Performance Tuning: Implement optimization techniques such as partitioning, caching, and lazy evaluation to enhance the performance of Spark jobs and reduce processing time. Methodology: Data Exploration and Preparation: Explore and preprocess the dataset to handle missing values, outliers, and data inconsistencies, ensuring data quality and reliability. Spark Environment Setup: Set up a Spark cluster either on-premises or on a cloud platform such as AWS or Azure, configuring the necessary resources and dependencies. Development of Spark Applications: Develop Spark applications using Scala, Python, or Java to implement various data processing and analytics tasks according to the project requirements. Testing and Validation: Test the Spark applications using sample datasets and validation techniques to ensure accuracy and reliability of the results. Deployment and Integration: Deploy the Spark applications into production environment and integrate them with existing systems and workflows for seamless operation. Deliverables: Technical Documentation: Provide detailed documentation covering the project architecture, design decisions, implementation details, and deployment instructions. Codebase: Deliver well-organized and documented codebase of the Spark applications developed during the project, along with unit tests and integration tests. Performance Metrics: Present performance metrics and benchmarks demonstrating the efficiency and scalability of the Spark-based solution compared to traditional approaches. Training and Support: Offer training sessions and support to the project stakeholders to enable them to effectively utilize and maintain the Spark-based solution.

1. Done by: Fatima Ali 9203
Zahraa Dokmak 9205
Sara Dokamk 9206
Presented to: Dr. Hussein Hazimeh
2023–2024
Kafka vs Spark vs Impala

2. The term "Big Data" refers to large
and complex datasets that cannot be easily managed,
processed, or analyzed using traditional data
processing tools
Big Data poses challenges such
as volume (the sheer amount of data), velocity
(the speed
at which data is generated and processed), variety
(the different types of data sources), and veracity
(the reliability and accuracy of the data)
Definition of Big Data
Challenges of Big Data

3. Log Aggregation:
It can be used
to aggregate
log data from
multiple sources
for centralized
monitoring
and analysis
Messaging System
for Microservices:
Kafka acts as
a highly scalable
and fault-tolerant
messaging
system
for communication
between microservices in
a distributed architecture
Real-time Data
Pipeline: Kafka
is used for collecting,
processing,
and delivering real-
time data
streams from various
sources
such as sensors,
applications,
and databases
Apache Kafka:
Apache Kafka is
an open-source distributed streaming
platform designed for building real-time
data pipelines and streaming applications

4. Topics: Logical
channels
for organizing
and partitioning
data
streams
Consumers:
Applications that
subscribe to and
process data from
Kafka topics
Producers:
Applications that
publish data
to Kafka
topics
Brokers: Kafka
servers
responsible
for storing
and managing data
partitions
Replication and Fault
Tolerance: Kafka
ensures data
durability and fault
tolerance through
data replication
across multiple
brokers13.
Architecture:

5. Kafka follows a publish-subscribe messaging model where producers
publish messages to topics, and consumers subscribe to topics to receive messages
in real-time
LinkedIn utilizes Kafka for real-time activity tracking, monitoring, and data
integration across various services and systems
How it Works
Case Study

6. Apache Spark is a fast
and general-purpose cluster
computing system
designed for large-scale data
processing and analytics
Large-scale Data Processing: Spark is used
for processing massive datasets in distributed
environments, enabling tasks like ETL (Extract,
Transform, Load) and batch processing
Real-time Stream Processing: Spark
Streaming allows for the processing of real-time data
streams with
low latency, making it suitable for applications like
real-time analytics and monitoring
Machine Learning and Graph Processing:
Spark
provides libraries for machine learning (MLlib)
and graph processing (GraphX), enabling advanced
analytics and algorithmic computations
Use Cases:
Definition and Purpose:
Apache Spark:

7. Architecture:
Directed Acyclic Graph
(DAG): Spark uses a DAG
execution engine
for optimizing and scheduling
data processing tasks
Resilient Distributed Dataset
(RDD): Spark's fundamental
data abstraction
for distributed processing
and fault tolerance
Components: Spark Core,
Spark SQL, Spark Streaming,
MLlib, and GraphX

8. Spark performs in-memory computation, caching data in memory across multiple
nodes for faster data processing and iterative algorithms
Netflix utilizes Spark for analyzing user behavior and preferences, powering
recommendation systems, and performing real-time analytics on streaming data
How it Works
Case Study

9. Apache Impala is
an open-source, high-performance
SQL query engine
for processing data stored in Hadoop
Distributed File
System (HDFS) and Apache HBase
Interactive Analytics: Impala enables
interactive
querying and analysis of large datasets stored
in Hadoop, providing low-latency responses to
ad-hoc SQL queries
Business Intelligence (BI) Reporting:
It can be used
for generating reports, dashboards,
and visualizations
using popular BI tools like Tableau and Power BI
Ad-hoc Queries on Hadoop Data:
Impala allows users
to perform ad-hoc SQL queries on raw
or processed
data stored in Hadoop, without requiring data
movement or transformation
Use Cases:
Definition and Purpose
Apache Impala:

10. Architecture:
Massively Parallel Processing (MPP): Impala
employs a distributed and parallel processing
architecture for executing SQL queries across
multiple nodes in a cluster
Coordination Layer and Execution Nodes: Impala
includes a coordinator node for query planning
and coordination, and multiple execution nodes
for parallel query execution

11. Impala executes SQL queries directly on data stored in Hadoop, bypassing the need
for intermediate data serialization and deserialization, resulting in low-latency query
responses
Airbnb utilizes Impala for real-time data exploration and analysis, enabling data scientists
and analysts to query and analyze large volumes of data stored in Hadoop for business
insights and decision-making
How it Works
Case Study

12. Overview: Kafka, Spark, and Impala can be integrated to build end-to-end big data processing pipelines
Spark for Data Processing
and Analytics:
Spark can consume data
from Kafka
topics, perform real-time
stream
processing or batch
processing, and then
store processed data
in Hadoop or other
storage systems
Kafka for Real-time Data
Ingestion: Kafka
can be used to ingest real-
time data
streams from various sources
into
a centralized platform
for further
processing
Impala for Interactive
SQL Querying:
Impala can directly query data
processed
by Spark, providing users with
interactive
SQL querying capabilities for ad-
hoc
analysis and reporting
Integration of Kafka, Spark, and Impala:

13. Scalability: Kafka, Spark, and Impala are designed
for horizontal scalability, allowing them to handle
increasing data volumes by adding more nodes
to the cluster
Fault Tolerance: All three technologies provide
fault tolerance mechanisms to ensure data
durability and system reliability in the face
of failures
In-memory Processing: Spark leverages
in-memory computation for faster data
processing, while Kafka and Impala also benefit
from distributed in-memory processing
for improved performance
Performance and Scalability:

14. Scalability Challenges: Managing and scaling
large clusters of Kafka, Spark, and Impala can
be complex and resource-intensive
Data Consistency and Durability: Ensuring data
consistency and durability, especially
in distributed environments like Kafka, can
be challenging and requires proper configuration
and monitoring
Complex Setup and Configuration: Setting up and
configuring Kafka, Spark, and Impala clusters
require expertise and careful consideration
of hardware, software, and network requirements
Resource Management and Optimization:
Optimizing resource utilization and performance
tuning in Spark and Impala clusters require
continuous monitoring and adjustment
of configurations
Challenges and Limitations:

15. Monitoring
and Logging: Implement
robust monitoring
and logging solutions
to track cluster
performance, resource
utilization, and system
health
Resource Allocation
and Cluster Sizing:
Properly allocate
resources such as CPU,
memory, and storage,
and size clusters
according to workload
requirements
and expected data
volumes
Data Partitioning
and Replication:
Use appropriate data
partitioning
and replication
strategies in Kafka
and Spark to ensure
data distribution
and fault tolerance
Best Practices: