Frameworks and Algorithms 2024

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What does Apache Spark rely on as its foundational data format?

  • Data Lakes
  • Rich Data Frames
  • Resilient Distributed Data Sets (correct)
  • Structured Query Language

What is one key advantage of using DataFrames in Spark?

  • They offer a similar interface to Pandas DataFrames. (correct)
  • They are limited to local processing.
  • They cannot handle distributed systems effectively.
  • They require a defined schema before data entry.

Which statement correctly describes schema management in Spark?

  • Spark allows for reading data without requiring a schema upfront. (correct)
  • Spark DataFrames require a schema to operate effectively.
  • Schemas in Spark are only used for structured JSON data.
  • Data Lakes in Spark require a predefined schema for data writing.

What primary function does Kubernetes serve in managing containers?

<p>It orchestrates the starting and stopping of containers. (C)</p> Signup and view all the answers

How can RDDs and DataFrames differ in their handling of schema?

<p>DataFrames require a schema, while RDDs do not. (A)</p> Signup and view all the answers

What does elastic computing in relation to Kubernetes imply?

<p>Compute resources can dynamically grow and shrink. (A)</p> Signup and view all the answers

What is a potential downside of using a distributed system without careful management?

<p>It can lead to unexpected high costs for processes. (B)</p> Signup and view all the answers

Which operation cannot be performed directly on an RDD in Spark?

<p>Directly analyze with SQL-like queries (A)</p> Signup and view all the answers

What is the output of the map step mentioned in the process?

<p>An RDD comprising an array of integers (D)</p> Signup and view all the answers

What does a Spark session allow you to do with data?

<p>Read data and infer or require schema (D)</p> Signup and view all the answers

What happens if the required schema does not match the data being loaded?

<p>It will throw an error or force the schema (D)</p> Signup and view all the answers

How does inferring schema in Spark's data frame compare to reading a CSV file in Pandas?

<p>It is more intelligent and operates in parallel (B)</p> Signup and view all the answers

What is a fundamental difference between an RDD and a DataFrame in Spark?

<p>RDDs do not support schema, while DataFrames do (A)</p> Signup and view all the answers

Which command is used to start the configuration of a Spark session?

<p>spark session create (A)</p> Signup and view all the answers

What type of view can be created from a Spark DataFrame to allow SQL operations?

<p>Temporary view or general view (D)</p> Signup and view all the answers

What does the process of converting unstructured data involve in Spark?

<p>Defining assumptions about the schema and producing structured data (A)</p> Signup and view all the answers

What type of typing does Python use that makes the data set API unnecessary?

<p>Duct typing (B)</p> Signup and view all the answers

What is the main purpose of Delta Lake in Data Bricks?

<p>To guarantee ACID transactions and safeguard data integrity (C)</p> Signup and view all the answers

Which of the following is NOT a feature of Data Bricks?

<p>Dedicated APIs for Java only (B)</p> Signup and view all the answers

How does Spark manage data loading from various distributed storage systems?

<p>By utilizing built-in connectors for different object stores (C)</p> Signup and view all the answers

Which characteristic differentiates RDDs from DataFrames in Spark?

<p>DataFrames support schema and types, while RDDs do not (D)</p> Signup and view all the answers

What is the functionality provided by the Data Set API introduced in 2015?

<p>Typed operations for data manipulation (A)</p> Signup and view all the answers

What aspect of Spark does the command 'spark-submit' primarily affect?

<p>Submitting jobs to the Spark cluster (A)</p> Signup and view all the answers

How does Spark facilitate machine learning operations with its tools?

<p>Through integration with ML Flow and Psyche-Learn (D)</p> Signup and view all the answers

Flashcards

RDD (Resilient Distributed Dataset)

A distributed collection of elements in Spark. It's a fundamental data structure in Spark.

Spark Dataframe

A distributed table in Spark. It provides a tabular view of data, is more structured than RDDs, and uses a schema.

Spark Session

An entry point to interact with Spark. It provides access to Spark's functionalities, including loading data and creating DataFrames.

Inferring schema

Automatically determining the structure (schema) of data from a Spark DataFrame. Spark figures out data types (e.g., integer, strings).

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Spark SQL

Using structured query language (SQL) to query data within a Spark Dataframe. Think of it as SQL for your distributed data.

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Unstructured Data

Data without a defined format or schema. No clear structure for analysis.

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Structured Data

Data that has a predefined form or schema. This allows for easier analysis.

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Dataframe vs RDD

DataFrames are built upon RDDs, extending upon the capabilities of RDDs to offer a more structured data type with schema.

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Data set API (Spark)

A way to interact with data in Spark, either without specifying types or by using types.

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Python and Data Typing

Python's dynamic typing allows type decisions at runtime, eliminating a need for special type declaration support, unlike languages like Java.

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Data Bricks

A commercial software solution built on top of Spark, offering simplified data manipulation using a Jupyter Notebook-like interface.

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Lake House (Data Bricks)

A data management approach that supports both structured (schema) and unstructured (no schema) data types.

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Delta Lake

Open-source software within Data Bricks that provides ACID (Atomicity, Consistency, Isolation, Durability) transactions to guarantee data integrity and stability.

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Data Source Support (Spark)

Spark's ability to read data from various distributed storage locations (AWS, Azure, Google Cloud).

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Spark Integration (with tools)

Spark's compatibility with existing data science tools like MLflow, allowing them to integrate/work with Spark.

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Jupyter Notebook Interface

Web-based interactive computing environment within Data Bricks, providing an intuitive way to use Spark for data exploration and analysis.

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What are containers?

Containers are like lightweight operating systems, stripped down to only what's needed to run a specific application. They allow you to package software and its dependencies, and run them consistently on different machines.

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What is Kubernetes?

Kubernetes is like a traffic cop for your containers. It manages and orchestrates the running of containers in a cloud environment, starting, stopping, and scaling them.

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How does Kubernetes make Spark applications more powerful?

Kubernetes enables Spark to run on a scalable platform. By dynamically allocating resources, Spark workloads can grow or shrink based on demand.

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What is a resilient distributed dataset (RDD)?

An RDD is a fundamental data structure in Spark that stores data in a distributed way, meaning it can be spread across multiple machines. It's flexible because it doesn't need a specific schema (structure).

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What is a Spark DataFrame?

A Spark DataFrame is a structured way to represent data in Spark. It's like a table with columns and rows, making it easier to analyze and manipulate data with SQL-like queries.

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What is the benefit of Spark DataFrames compared to RDDs?

DataFrames provide a more structured way of working with data, offering a familiar interface similar to Pandas dataframes. This makes it easier to analyze data in a structured way.

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What is the relationship between RDDs and DataFrames?

DataFrames are built upon RDDs, essentially adding a structured layer on top. RDDs provide the underlying distributed storage, while DataFrames offer the ability to work with data in a more structured way.

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What is a data lake?

A data lake is a massive storage space for data in any format. Unlike traditional data warehouses, it doesn't require a predefined schema (structure).

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Study Notes

w02-01-FrameworksAndAlgorithms-2024

  • The lecture covers various topics, including different frameworks and algorithms for solving problems.
  • The lecture does not discuss databases, but focuses on files distributed across multiple computers.
  • Key challenges when dealing with data from various sources include differing data formats.
  • Data streams from sensors need analysis, often resulting in dashboards for data visualization.
  • Extracting features from data is a crucial aspect; features are characteristics of the data, for example, a higher than normal gearbox temperature on a wind turbine could indicate a failure.
  • Data quality checks are essential for ensuring data reliability and accuracy; these checks verify data type correctness and handle potentially incorrect input data.
  • There may be multiple consultancies; such as for train track networks providing data that may need to be transformed and combined.
  • Data analysis may require system behavior or user behavior analysis to assess the condition or health of a system.
  • Log analysis may be necessary for certain tasks, such as examining server logs or other relevant logs within a system.
  • The lecture mentions examples like wind farms, marine boats, complicated machinery, patient cases (predicting the need for the patient to be moved to a hospital, e.g. Crohn's, inflammatory bowel disease)

Data Management Systems

  • Data lakes are a generic storage for unstructured data (large amounts of data) found on the Internet - e.g. log files, social media data.
  • Data lakes typically have no schema or structure, meaning they contain mixed or non-specified data formats, but they are used to add more convenience when processing.
  • Data warehouses are structured data storage that allows queries and data retrieval.
  • Data warehousing involves copies of data that are in the back end and then loaded into the data warehouse.
  • Data warehousing typically uses relational databases for structured data, so you will need to specify a schema.
  • Data warehousing may require replicating data to avoid performance issues with the main or primary database.

Additional Information

  • Data structures for machine learning may use a neural network or decision tree.
  • Tools such as Tableau and Power BI may be used to generate dashboards to be viewed by top-level management (e.g., product health, company performance indicators).
  • Spark is a data processing system that is commonly used in commercial settings.
  • Spark excels in speeding up data analysis.
  • Spark is better at handling smaller data sets.
  • Spark caches data in memory to enhance performance.
  • Spark allows various languages such as Python, and SQL or R) for performing analysis (not just Java).
  • Pandas are frequently used as a data analysis framework.
  • Python allows flexibility by implementing a simple algorithm (such as MapReduce) from scratch.
  • Python code also enables reading data and writing results into RDDs (Resilient Distributed Datasets).
  • Spark is particularly efficient for interactive data mining.
  • Apache Spark framework is efficient for processing large datasets, and can also process data across multiple data nodes.

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