Beginning Apache Spark Using Azure Databricks: Unleashing Large Cluster Analytics in the Cloud (Paperback)

· 제목 : Beginning Apache Spark Using Azure Databricks: Unleashing Large Cluster Analytics in the Cloud (Paperback) 
· 분류 : 외국도서 > 경제경영 > 산업 > 컴퓨터
· ISBN : 9781484257807
· 쪽수 : 274쪽
· 출판일 : 2020-06-12

Chapter 1, Introduction to large scale data analytics.

Chapter goal: Reader should understand the data analytics and the workflow.

·         So what is data analysis?

·         The process of running a data analysis project.

·         Real world example.

·         What about data scientists?

Chapter 3, Distributed processing, Spark and Databricks.

Chapter goal: Reader should understand Spark on a high level and what Databricks is.

·         Computational history.

·         Scale up vs scale out.

·         Traditional analytics platforms.

·         The power of Spark.

·         The simplicity of Databricks.

Chapter 4, Getting started with Databricks.

Chapter goal: Reader should understand how to get a Databricks installation up and running.

·         A short introduction to Spark architecture

·         Setting up a cloud account.

·         Getting Databricks running.

·         Finally ? time to start Databricks.

Chapter 5, Workspaces, Clusters and Notebooks.

Chapter goal: Reader should understand how to find his or her way around the UI.

·         Finding your way around the user interface.

·         Starting the engine ? cluster creation.

·         A short note about checkboxes and configurations.

·         Picking the right notebook.

·         Keeping track of the workspace

Chapter 6, Getting data into Databricks.

Chapter goal: Reader should understand the many ways they can get data into Databricks.

·         Filesystems and data formats.

·         Working with schemas.

·         Importing Excel data.

·         Picking up information from the web.

·         Mounting the cloud data lake.

Chapter 7, Querying data using SQL.

Chapter goal: Reader should understand how to use SQL for looking and manipulating data.

·         Databases and tables in the Hive Metastore.

·         Pulling some data.

·         Joining, grouping and summarizing.

·         Views and procedures.

·         Hey ? what’s up with updates?

Chapter 8, Python (and a little bit of Scala and R).

Chapter goal: Reader should understand how to use Python for playing around with data.

·         An introduction to Dataframes.

·         Python vs SQL.

·         Working with data

·         But what about Scala and R?

Chapter 9, ETL and more advanced data wrangling.

Chapter goal: Reader should understand even more around manipulating data.

·         Stars and snowflakes.

·         Cleaning the data.

·         Speeding things up.

·         Working with partitions.

·         Setting parameters.

Chapter 10, Connecting from afar.

Chapter goal: Reader should understand how they can connect to Databricks from other tools.

·         Setting up ODBC and JDBC.

·         Getting to know the API:s.

·         Example: Connecting Power BI.

Chapter 11, Running in Production

Chapter goal: Reader should understand how to run and monitor jobs in production.

·         How to set up jobs.

·         Working with schedules.

·         Monitoring the jobs.

Chapter 12, Removing the training wheels.

Chapter goal: Reader should get to know the more advanced options.

·         Security in Databricks.

·         Machine learning using MLlib.

·         Going full ACID with Delta lake.

·         High speed streaming.

·         A deep dive into Spark architecture.