Data Ingestion and Processing Concepts

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Questions and Answers

What is primarily covered in Unit 2?

Data Modelling and Metadata Management

Distributed Data Techniques

Data Quality and Governance

Data Protection and Security (correct)

Which schema types are discussed in Unit 5?

All Forms of Data Governance

Entity Relationship Model and Star Schema (correct)

Data Normalization and Data Warehousing

Star Schema and Snowflake Schema (correct)

Which process is related to distributed data reliability?

Data virtualization frameworks

Data replication (correct)

Data encryption methods

Data masking techniques

What type of metadata is NOT mentioned as being covered?

Technical metadata Signup and view all the answers

Which of the following units focuses on Data Governance?

Unit 4 Signup and view all the answers

Which of the following topics pertains to Unit 6?

Metadata repositories Signup and view all the answers

What principle is NOT included in Unit 2's focus on Data Protection?

Data Archiving Signup and view all the answers

What is the primary focus of Unit 3?

Distributed Data and Its Management Signup and view all the answers

What is the main characteristic of a Directed Acyclic Graph (DAG) in data processing frameworks?

It has clearly defined dependencies between tasks. Signup and view all the answers

How does batch processing typically gather data?

At regular time intervals for centralized storage. Signup and view all the answers

What is a key advantage of streaming data ingestion over batch processing?

It allows data to be processed in near real-time. Signup and view all the answers

What does an orchestrator do in a data processing framework?

It supervises the execution of pipeline tasks. Signup and view all the answers

Which process involves an event-driven approach in data processing?

Streaming data ingestion that processes data as it becomes available. Signup and view all the answers

Why is merely using time intervals to define batch and stream processing considered flawed?

It doesn’t account for the varying demands of different applications. Signup and view all the answers

What type of architecture is often implemented for event-driven solutions?

Publish-subscribe architecture. Signup and view all the answers

Which component is not typically part of an orchestrator in a data processing framework?

Data storage for raw input data. Signup and view all the answers

Which of the following best describes data heterogeneity?

The variation in data types, formats, and storage solutions from multiple sources. Signup and view all the answers

Which type of data is considered structured?

An Excel spreadsheet with predefined columns. Signup and view all the answers

What distinguishes semi-structured data from structured data?

Semi-structured data has a partial schema but lacks full definition by a data model. Signup and view all the answers

Which of the following is an example of structured data?

A table of employee records in an SQL database. Signup and view all the answers

How is semi-structured data commonly used?

It is often employed in web applications and IoT devices. Signup and view all the answers

What form do structured data typically take?

Data organized into rows and columns within tables. Signup and view all the answers

Which of the following data formats is considered unstructured?

Weblog entries. Signup and view all the answers

What is a common source for integrating semi-structured data?

APIs that yield information from IoT devices. Signup and view all the answers

What is the primary role of the NameNode in HDFS architecture?

To manage access and metadata of resources. Signup and view all the answers

How does HDFS ensure fault tolerance and high availability?

By utilizing commodity hardware that can fail occasionally. Signup and view all the answers

Which of the following describes how data is organized in HDFS?

Data is organized hierarchically in directories and subdirectories. Signup and view all the answers

What is the function of DataNodes in the HDFS architecture?

To store the actual data blocks. Signup and view all the answers

How does MapReduce function within the HDFS ecosystem?

It splits tasks into map and reduce parts for parallel processing. Signup and view all the answers

Which framework is responsible for managing resources during data processing in HDFS?

YARN Signup and view all the answers

What advantage does data distribution across several nodes provide in HDFS?

High potential for parallel data processing. Signup and view all the answers

What infrastructure does HDFS primarily rely on for storage?

Commodity hardware with several discs. Signup and view all the answers

What is the primary purpose of assigning weights in artificial neural networks (ANNs)?

To emphasize or inhibit connections between nodes Signup and view all the answers

What distinguishes a multi-layer perceptron from a simple perceptron?

Multi-layer perceptrons can handle nonlinear separable problems Signup and view all the answers

What is the main function of the backpropagation algorithm in neural networks?

To update weights based on error minimization Signup and view all the answers

How does deep learning differ from traditional neural networks?

It contains multiple hidden layers, creating a deeper architecture Signup and view all the answers

What is a notable advantage of using GPUs for deep learning algorithms?

Greater parallel processing capability Signup and view all the answers

In the context of reinforcement learning, what role do rewards play?

They provide feedback to optimize the behavior of agents Signup and view all the answers

What is transfer learning primarily focused on in neural networks?

Retraining pre-trained models for specific use cases Signup and view all the answers

Which of the following best describes Convolutional Neural Networks (CNNs)?

Models effective in extracting features for image recognition Signup and view all the answers

Study Notes

Data Ingestion

Data is collected from various sources with different formats.
Data can be structured, semi-structured or unstructured.
Structured data conforms to a well-defined data schema, such as a person's name, address, and date of birth.
Structured data is usually stored in relational SQL databases, structured text files like CSV, or binary files like Excel spreadsheets.
Semi-structured data has some structure but not entirely defined by a data model, such as HTML, XML, or JSON files.
Semi-structured data is often used on the web and with IoT devices.

Data Processing

Data processing frameworks are designed to store, access, and process large amounts of data efficiently.
Modern frameworks distribute storage and processing over several nodes, allowing for parallel processing.
Data processing is often modeled as a Directed Acyclic Graph (DAG), where tasks have input, output, and dependencies.
ETL (Extract, Transform, Load) is a traditional approach for batch processing.
Streaming data ingestion processes data in real-time or near real-time.
Event-driven solutions are often implemented as a publish-subscribe architecture based on messages/events.

Hadoop

HDFS (Hadoop Distributed File System) is a distributed file system that stores data on multiple nodes.
HDFS scales both vertically (by increasing node capacities) and horizontally (by adding nodes to the cluster).
HDFS uses a master-slave configuration with NameNodes and DataNodes.
NameNodes manage access to resources and store system metadata.
DataNodes are responsible for actual data storage.
Hadoop implements high tolerance for failures and high availability.
Data processing within a distributed architecture is highly parallelizable.

MapReduce

MapReduce is a processing framework that splits large tasks into mapping and reducing parts.
MapReduce parallelizes individual steps, allowing for operations on massive datasets.

YARN

YARN (Yet Another Resource Negotiator) manages resources for data processing.
YARN parallelizes processing for distributed computing by dividing computations into smaller tasks distributed across nodes.

Artificial Neural Networks (ANNs)

ANNs are inspired by the human brain and consist of interconnected nodes called neurons.
Each neuron receives inputs from other neurons, processes them with a weighted sum, and activates an output if a certain threshold is passed.
ANNs are often elaborated using activation functions and multiple layered architectures.
The backpropagation algorithm is used for training ANN models to find optimal weights.
Deep learning involves ANNs with a significant number of hidden layers.

Convolutional Neural Networks (CNNs)

CNNs are Deep Learning algorithms that are particularly efficient for object recognition in images.
CNNs have multiple layers that automatically extract informative features.

Reinforcement Learning

Reinforcement Learning aims to optimize decision-making through rewards for agents in a simulation.
Reinforcement learning can use deep learning algorithms but also other algorithms.

Transfer Learning

Transfer learning involves re-training pre-trained general-purpose neural networks to match specific use cases.
This is achieved by removing the last layers of a network and training it with specific data.

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Description

Explore the essential concepts of data ingestion and processing in this quiz. Learn about structured, semi-structured, and unstructured data, along with the frameworks used for efficient data handling. Test your knowledge on ETL processes and the modeling of data as Directed Acyclic Graphs.