Questions and Answers
What is the primary language used to define a state machine in AWS Step Functions?
Which AWS service can trigger an AWS Step Functions workflow on a schedule?
Which component is NOT required when using AWS Glue workflows exclusively?
What is one primary benefit of AWS Step Functions being serverless?
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At what time does the di-raw-customer-crawler start its operation in the given workflow?
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Which functionality does AWS Step Functions provide for controlling workflows?
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What does the Glue ETL job do in the workflow?
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When initiating a state machine in AWS Step Functions, what format is used for input data?
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What triggers the di-curated-customer-crawler in the workflow?
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How can users create a workflow in AWS Step Functions without writing JSON?
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What is a limitation of AWS Glue workflows?
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Which task could be performed by a state in a Step Functions workflow?
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What service can be used to create more complex workflows other than AWS Glue?
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What allows each step in a Glue workflow to update the state information for subsequent steps?
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What role does the Amazon States Language (ASL) play in AWS Step Functions?
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How does error handling in AWS Step Functions enhance workflows?
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What task do AWS Glue Crawlers perform?
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Which of the following is a primary benefit of using AWS Glue for running Apache Spark?
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In addition to Glue Crawlers, how else can you add databases and tables to the Glue catalog?
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Which of the following file types can AWS Glue Crawlers identify?
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What does Amazon EMR manage for big data processing?
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What is a key difference between AWS Glue and Amazon EMR for running Apache Spark?
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What is the likely outcome of using AWS Glue for running Apache Spark?
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Which service requires that users specify the compute cluster for running Apache Spark?
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What triggers the CloudWatch event that starts the Step Functions state machine?
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What does the 'Process Incoming File' step do in the state machine?
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Which field in the JSON data is checked in the 'Did Job Succeed?' step?
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What happens if the 'jobStatus' field is set to 'succeeded'?
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What is the role of the 'Job Failed' step in the state machine?
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What does the error state step deal with?
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Which of the following programming languages is supported by Apache Airflow for managing workflows?
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What is a challenge mentioned regarding the deployment and management of Apache Airflow?
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Which of the following AWS services is designed to transform data using Python shell scripts and Apache Spark code?
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What is a key advantage of using a transient cluster for big data processing in the cloud?
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Which of the following frameworks is NOT included in an Amazon EMR cluster?
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Which AWS service supports running Hadoop and Spark applications and is considered cost-effective for processing large amounts of data?
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What type of environment is Amazon EMR most suitable for?
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Which service would you use to automate data ingestion from a variety of sources including data stores, streaming services, and file systems?
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Which AWS service includes a central repository for metadata associated with datasets known as a data catalog?
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For a team with experience running Apache Spark environments, and requirements for fine-tuning compute power and settings, which AWS service is most suitable?
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What is required for each EMR cluster?
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Which AWS Glue component provides a centralized logical representation of data in an Amazon S3 data lake?
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What is the function of Glue crawlers in AWS Glue?
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Which AWS service allows data scientists to run SQL queries against a data lake?
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What is a workflow in AWS Glue?
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What is NOT a feature of Amazon Athena?
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Which environment is NOT included in AWS Glue?
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For what purpose might business users utilize AWS services?
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Why can orchestrating data pipelines be complex in an organization?
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What is the primary role of SQL in Amazon Athena?
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How are Glue crawlers typically configured?
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Which user group is most likely to use Amazon Athena for querying data?
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What purpose does a master node in an EMR cluster serve?
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What benefit does querying data directly from an Amazon S3 data lake provide?
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What controls what tasks need to be run and where and when to run those tasks in an MWAA environment?
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Which component in an MWAA environment is responsible for executing tasks?
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Where does the meta-database run in an MWAA environment?
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What is the main function of the web server in an MWAA environment?
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How does MWAA handle scaling of workers?
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What should be considered when migrating from an on-premises environment to MWAA?
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What is the primary difference between MWAA and serverless environments like Amazon Step Functions in terms of billing?
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Which of the following statements accurately describes Amazon Athena?
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What language is predominantly used in Amazon Athena for querying?
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What can data scientists create in Amazon Athena to increase data analysis efficiency?
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What year did Apache Airflow become a top-level Apache project?
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Which of the following integrations is NOT supported by Apache Airflow?
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What language is used to create processing pipelines in Apache Airflow?
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Who created Apache Airflow, and when?
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What is a primary benefit of using AWS Managed Workflows for Apache Airflow (MWAA)?
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What challenge does AWS Managed Workflows for Apache Airflow (MWAA) aim to address?
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Which tool enables a data consumer to query datasets in the data lake through the AWS Management Console?
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What functionality does Athena Federated Query provide?
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Amazon Redshift is designed for which type of workloads?
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Which service can connect to Amazon Athena using a JDBC driver?
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What does Amazon Redshift Spectrum enable users to do?
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Which service helps to store metadata about data sources enabling queries in Amazon Athena?
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What is one advantage of using Amazon Redshift for large datasets?
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Which services are provided pre-built connectors by Amazon for Athena Federated Query?
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Why might a data consumer only load a subset of data into Amazon Redshift?
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What is a common use case for OLAP workloads in Amazon Redshift?
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Study Notes
AWS Glue Crawlers
- AWS Glue crawlers are processes that examine a data source, automatically infer the schema and other information, and populate the AWS Glue Data Catalog.
- They can be pointed at an S3 location, examine a portion of each file, identify the file type, and add the information to the catalog.
Overview of Amazon EMR
- Amazon EMR provides a managed platform for running popular open source big data processing tools, such as Apache Spark, Apache Hive, and Presto.
- It takes care of the complexities of deploying these tools and managing the underlying compute resources.
- EMR can be used to run Apache Spark, but it requires more configuration and fine-tuning compared to AWS Glue.
The AWS Data Engineer's Toolkit
- AWS Glue is a serverless service for transforming data, with a data catalog, crawlers, and ETL jobs.
- Amazon EMR can be used to run Hadoop and Spark applications, and is a cost-effective option for processing massive amounts of data.
- Amazon S3 can be used to store large amounts of data, with high availability and integration with other AWS services.
AWS Services for Orchestrating Big Data Pipelines
- AWS Glue workflows are used to orchestrate Glue components, such as ETL jobs and crawlers.
- AWS Step Functions can be used to create complex workflows that integrate with many AWS services, with a serverless and consumption-based pricing model.
- Amazon Managed Workflows for Apache Airflow (MWAA) is a managed service for running Apache Airflow, with automatic scaling and integration with AWS services.
Overview of AWS Glue Workflows
- AWS Glue workflows consist of an ordered sequence of steps that run Glue crawlers and ETL jobs.
- Each step can retrieve and update state information, enabling one step to provide state information for a subsequent step.
- Glue workflows can only be used to orchestrate Glue components.
Overview of AWS Step Functions
- AWS Step Functions is a service for creating complex workflows that integrate with many AWS services.
- It uses JSON to define a state machine, with a visual interface for creating and managing workflows.
- Step Functions can run multiple tasks, with choices, waits, and error handling.
Overview of Apache Airflow
- Apache Airflow is an open-source platform for managing data engineering workflows.
- It enables users to program workflows in various programming languages and trigger different tools.
- Airflow has a wide variety of integrations with various tools and platforms, including AWS services.
Managed Workflows for Apache Airflow (MWAA)
- MWAA is a managed service for running Apache Airflow, with automatic scaling and integration with AWS services.
- It consists of a scheduler, workers, meta-database, and web server.
- MWAA enables users to easily deploy a managed version of Apache Airflow, with scaling and resilience.
AWS Services for Consuming Data
- Amazon Athena is a serverless query service that allows users to run standard SQL queries against data in a data lake or other data sources.
- Data scientists can use Athena to analyze datasets, while data analysts can use it to load data into a high-performance data warehouse.
- Business users can access data via visualization tools, representing data as graphs, charts, and other visuals.### SQL in Data Analytics
- SQL allows proficient users to easily and quickly draw information from large relational datasets by combining tables, filtering results, and performing aggregations.
- Data scientists and analysts use SQL to explore and understand datasets that may be useful to them.
The AWS Data Engineer's Toolkit
- Many tools interface with SQL data sources using JDBC or ODBC drivers.
- Amazon Athena enables data consumers to query datasets in data lakes or connected sources through the AWS Management Console or JDBC/ODBC drivers.
- Graphical SQL query tools, like SQL Workbench, can connect to Amazon Athena through JDBC drivers.
- ODBC drivers allow programmatically connecting to Amazon Athena and running SQL queries in code.
Athena Federated Query
- Athena Federated Query enables building connectors to query beyond S3 data lakes, including other data sources.
- Amazon provides pre-built, open-source connectors for Athena to connect to sources like Amazon DynamoDB, Amazon managed relational database engines, and Amazon CloudWatch Logs.
- Athena Federated Query allows running a single SQL statement that combines data from multiple sources, such as active orders from DynamoDB, customer data from PostgreSQL, and historical order data from S3 data lakes.
Overview of Amazon Redshift and Redshift Spectrum
- Amazon Redshift is a cloud-based data warehouse designed for reporting and analytic workloads (OLAP).
- Redshift provides a clustered environment for fast performance with complex joins across multiple large tables.
- Redshift is ideal for reporting and visualization services working with large datasets.
- Amazon Redshift Spectrum enables querying more historical data stored in a data lake.
- Redshift Spectrum allows querying data in Redshift clusters and data lakes, enabling a single query to include both.
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Description
Learn about AWS Glue crawlers and Amazon EMR, two powerful tools for processing and managing big data. Understand how they work and their applications.