Posts Tagged Data Warehouse

Lakehouse Data Modeling using dbt, Amazon Redshift, Redshift Spectrum, and AWS Glue

Learn how dbt makes it easy to transform data and materialize models in a modern cloud data lakehouse built on AWS

Introduction

Data lakes have grabbed much of the analytics community’s attention in recent years, thanks to an overabundance of VC-backed analytics startups and marketing dollars. Nonetheless, data warehouses, specifically modern cloud data warehouses, continue to gain market share, led by SnowflakeAmazon RedshiftGoogle Cloud BigQuery, and Microsoft’s Azure Synapse Analytics.

Several factors have fostered the renewed interest and appeal of data warehouses, including the data lakehouse architecture. According to Databricks, “a lakehouse is a new, open architecture that combines the best elements of data lakes and data warehouses. Lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse directly on top of low-cost cloud storage in open formats.” Similarly, Snowflake describes a lakehouse as “a data solution concept that combines elements of the data warehouse with those of the data lake. Data lakehouses implement data warehouses’ data structures and management features for data lakes, which are typically more cost-effective for data storage.

dbt

In the following post, we will explore the use of dbt (data build tool), developed by dbt Labs, to transform data in an AWS-based data lakehouse, built with Amazon Redshift, Redshift Spectrum, AWS Glue, and Amazon S3. According to dbt Labs, “dbt enables analytics engineers to transform data in their warehouses by simply writing select statements. dbt handles turning these select statements into tables and views.” Further, “dbt does the T in ELT (Extract, Load, Transform) processes — it doesn’t extract or load data, but it’s extremely good at transforming data that’s already loaded into your warehouse.

This post’s project, displayed in dbt Cloud

Amazon Redshift

According to AWS, “Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes using AWS-designed hardware and machine learning to deliver the best price-performance at any scale.” AWS claims Amazon Redshift is the most widely used cloud data warehouse.

Amazon Redshift Spectrum

According to AWS, “Redshift Spectrum allows you to efficiently query and retrieve structured and semi-structured data from files in Amazon S3 without having to load the data into Amazon Redshift tables.” Redshift Spectrum tables define the data structure for the files in Amazon S3. The external tables exist in an external data catalog, which can be AWS Glue, the data catalog that comes with Amazon Athena, or an Apache Hive metastore.

dbt can interact with Amazon Redshift Spectrum to create external tables, refresh external table partitions, and access raw data in an Amazon S3-based data lake from the data warehouse. We will use dbt along with the dbt package, dbt_external_tables, to create the external tables in an AWS Glue data catalog.

Prerequisites

Prerequisites to follow along with this post’s demonstration include:

  • Amazon S3 bucket to store raw data;
  • Amazon Redshift or Amazon Redshift Serverless cluster;
  • AWS IAM Role with permissions to Amazon Redshift, Amazon S3, and AWS Glue;
  • dbt Cloud account;
  • dbt CLI (dbt Core) and dbt Amazon Redshift adapter installed locally;
  • Microsoft Visual Studio Code (VS Code) with dbt extensions installed;

The post’s demonstration uses dbt Cloud, VS Code, and the dbt CLI interchangeably with the project’s GitHub repository as a source. Follow along with the demonstration using any or all of these three dbt options.

Example of this project in VS Code with dbt extensions installed

Cost Warning!

Be careful when creating a new, provisioned Amazon Redshift cluster for this demonstration. The suggested default Production cluster with two ra3.4xlarge on-demand compute nodes and AQUA (Redshift’s Advanced Query Accelerator) enabled is estimated at $4,694/month ($3.26/node/hour). For this demonstration, choose the minimum size provisioned Redshift cluster configuration of one dc2.large on-demand compute node, estimated to cost $180/month ($0.25/node/hour). Be sure to delete the cluster when the demonstration is complete.

Creating a new Amazon Redshift cluster

Amazon Redshift Serverless Option

AWS recently announced the general availability (GA) of Amazon Redshift Serverless on July 12, 2022. Amazon Redshift Serverless allows data analysts, developers, and data scientists to run and scale analytics without having to provision and manage data warehouse clusters. dbt is fully compatible with Amazon Redshift Serverless and is an alternative to provisioned Redshift for this demonstration. According to AWS, Amazon Redshift Serverless measures data warehouse capacity in Redshift Processing Units (RPUs). You pay for the workloads you run in RPU-hours on a per-second basis (with a 60-second minimum charge), including queries that access data in open file formats in Amazon S3.

Source Code

All the source code demonstrated in this post is open source and available on GitHub.

Sample Data

This demonstration uses the TICKIT sample database provided by AWS and designed for use with Amazon Redshift. This sample database application tracks sales activity for the fictional online TICKIT website, where users buy and sell tickets for sporting events, shows, and concerts. The database consists of seven tables in a star schema: five dimension tables and two fact tables. A clean copy of the raw TICKIT data, formatted as pipe-delimited text files, is included in this GitHub project. Use the following shell commands to copy the raw data to Amazon S3:

unzip tickit_data.zip
s3_bucket="open-data-lake-demo-us-east-1"
declare -a TableArray=("category" "date" "event" "listing" "sale" "user" "venue")
for table in "${TableArray[@]}"
do
aws s3 cp ./raw_tickit_data/$table.txt s3://$s3_bucket/$table/
done

Prepare Amazon Redshift for dbt

Create New Database

Create a new Redshift database to use for the demonstration, demo.

create database demo
with owner awsuser;

Create Database Schemas

Within the new Redshift database,demo, create the external schema, tickit_external, and the corresponding external AWS Glue Data Catalogtickit_dbt, using the CREATE EXTERNAL SCHEMA Redshift SQL command. Make sure to update the command to reflect your IAM Role’s ARN. Next, create the schema that will hold our dbt models, tickit_dbt. Lastly, as a security best practice, drop the default public schema.

create external schema tickit_external
from data catalog
database 'tickit_dbt'
iam_role 'arn:aws:iam::<your_account_id>:role/<your_cluster_permissions_role>'
create external database if not exists;
create schema tickit_dbt;
drop schema public;

From the AWS Glue console, we should observe a new tickit_dbt AWS Glue Data Catalog. The description shown below was manually added after the catalog was created.

Newly created AWS Glue Data Catalog

Create dbt Database User and Group

As a security best practice, create a separate database dbt user and dbt group. We are assigning a completely arbitrary connection limit of ten. Then, apply the grants to allow the dbt group access to the new database and schemas. Lastly, change the two schema’s owners to the dbt.

create user dbt with password '<your_password>'
nocreatedb nocreateuser syslog access restricted
connection limit 10;
create group dbt with user dbt;
external tables
grant usage on schema tickit_external to group dbt;
grant create on schema tickit_external to group dbt;
grant all on all tables in schema tickit_external to group dbt;
dbt models: tables and view
grant usage on schema tickit_dbt to group dbt;
grant create on schema tickit_dbt to group dbt;
grant all on all tables in schema tickit_dbt to group dbt;
reassign schema ownership to dbt
alter schema tickit_dbt owner to dbt;
alter schema tickit_external owner to dbt;

Alternately, we could use an IAM Role with a SAML 2.0-compliant IdP.

Initialize and Configure dbt for Redshift

Next, configure your dbt Cloud account and dbt locally with your Amazon Redshift connection information using the dbt init command. On a Mac, this configuration is stored in the /Users/<your_usernama>/.dbt/profiles.yml file. You will need your Redshift cluster host URL, port, database, username, and password. With your local install of dbt, we can use the dbt debug command to confirm the new configuration.

Confirming configuration using dbt debug command

Project Structure

The GitHub project structure follows many of the best practices outlined in dbt Labs’ Best Practice Guide. Data models in the models directory is organized into the recommended stagingintermediate, and marts subdirectories (aka layers).

Project structure for data models

From a data lineage perspective, in this project, the staging layer’s data models depend on the external tables (AWS Glue/Amazon Redshift Spectrum). The intermediate layer’s data models depend on the staging models. The marts layer’s data models depend on staging and intermediate models.

dbt Cloud’s Lineage Graph showing an example of data model relationships

Install dbt Packages

The GitHub project’s packages.yml contains a few commonly recommended packages. The only one required for this post is the dbt-labs/dbt_external_tables package. Make sure your project is referring to the latest version of the package.

packages:
package: dbt-labs/dbt_external_tables
version: 0.8.0
package: dbt-labs/codegen
version: 0.7.0
package: dbt-labs/dbt_utils
version: 0.8.6
package: dbt-labs/redshift
version: 0.6.1
view raw packages.yml hosted with ❤ by GitHub

Use the dbt deps command to install the packages locally.

Installing dbt packages

External Tables

The _tickit__sources.yml file in the models/staging/tickit/external_tables/ model’s subdirectory defines the schema and S3 location for each of the seven external TICKIT database tables: category, date, event, listing, sale, user, and venue. You will need to update this file to reflect the name of your Amazon S3 bucket, in seven places.

version: 2
sources:
name: tickit_external
description: Sales activity for the fictional TICKIT web site, where users buy and sell tickets online for sporting events, shows, and concerts.
database: demo
schema: tickit_external
loader: s3
tables:
name: category
description: dimension table – TICKIT categories
external:
location: "s3://<your_s3_bucket_name>/raw_tickit_data/category/"
row_format: >
serde 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
with serdeproperties (
'separatorChar'='|'
)
table_properties: "('skip.header.line.count'='1')"
columns:
name: catid
data_type: int
description: primary key
tests:
unique
not_null
name: catgroup
data_type: varchar(20)
name: catname
data_type: varchar(20)
name: catdesc
data_type: varchar(50)

Execute the command, dbt run-operation stage_external_sources, to create the seven external tables in the AWS Glue Data Catalog. This command is part of the dbt_external_tables package we installed earlier. It iterates through all source nodes, creates the tables if missing, and refreshes metadata.

Creating external tables in AWS Glue Data Catalog

If we failed to run the previous SQL statements to set schema ownership to the dbt user, the following error will likely occur.

Typical error resulting from incorrect ownership of the external_table schema

Once the command completes, we should observe seven new tables in the AWS Glue Data Catalog.

Seven new tables in the AWS Glue Data Catalog

Examining one of the AWS Glue data catalog tables, we can observe how the configuration in the _tickit__sources.yml file was used to define the table’s properties and schema. Note the Location field indicates where the underlying data is located in our Amazon S3 bucket.

The ‘category’ table in the AWS Glue Data Catalog

Staging Layer

In their best practices guide, dbt describes the staging layer in the following manner: “you can think of the staging layer as condensing and refining this material into the individual atoms we’ll later build more intricate and useful structures with.” The staging data models are the base tables and views we will use to build more complex aggregations and analytics queries in Redshift. The schema.yml file, also in the models/staging/tickit/ model’s subdirectory, defines seven late-binding views, modeled by dbt, to be created in Amazon Redshift.

version: 2
models:
name: stg_tickit__categories
description: Late binding view of external dimension table – TICKIT category
name: stg_tickit__dates
description: Late binding view of external dimension table – TICKIT date
name: stg_tickit__events
description: Late binding view of external dimension table – TICKIT event
name: stg_tickit__listings
description: Late binding view of external fact table – TICKIT listing
name: stg_tickit__sales
description: Late binding view of external fact table – TICKIT sale
name: stg_tickit__users
description: Late binding view of external dimension table – TICKIT user
name: stg_tickit__venues
description: Late binding view of external dimension table – TICKIT venue
view raw schema.yml hosted with ❤ by GitHub

The staging model’s SQL statements also follow many of dbt’s best practices. Below, we see an example of the stg_tickit__sales model (stg_tickit__sales.sql). This model performs a SELECT from the external sale table in the external_table schema. The model performs column renaming and basic calculations.

{{ config(materialized='view', bind=False) }}
with source as (
select * from {{ source('tickit_external', 'sale') }}
),
renamed as (
select
saleid as sale_id,
listid as list_id,
sellerid as seller_id,
buyerid as buyer_id,
eventid as event_id,
dateid as date_id,
qtysold as qty_sold,
round (pricepaid / qtysold, 2) as ticket_price,
pricepaid as price_paid,
round((commission / pricepaid) * 100, 2) as commission_prcnt,
commission,
(pricepaid commission) as earnings,
saletime as sale_time
from
source
where
sale_id IS NOT NULL
order by
sale_id
)
select * from renamed

The the dbt run command, according to dbt, “executes compiled SQL model files against the current target database. dbt connects to the target database and runs the relevant SQL, required to materialize all data models using the specified materialization strategies.” Instead of using the dbt run command to create all the project’s tables and views at once, for now, we are limiting the command to just the models in the ./models/staging/tickit/ directory using the --select optional argument. Execute the dbt run --select staging command to materialize the seven corresponding staging tables in Amazon Redshift.

The Staging layer’s data models successfully materialized in Redshift

Once the command completes, we should observe seven new views in Amazon Redshift demo database’s tickit_dbt schema with the stg_ prefix.

Amazon Redshift Query Editor v2 Console

Selecting from any of the views should return data.

Amazon Redshift Query Editor v2 Console

Late Binding Views

This demonstration uses late binding views for staging and intermediate layer models. According to dbt, “using late-binding views in a production deployment of dbt can vastly improve the availability of data in the warehouse, especially for models that are materialized as late-binding views and are queried by end-users, since they won’t be dropped when upstream models are updated. Additionally, late binding views can be used with external tables via Redshift Spectrum.

Alternatively, we could define the seven staging models as tables instead of late binding views. Once created as tables, the dependent intermediate and marts views will not require a late-binding reference, as in this project.

Intermediate Layer

In their best practices guide, dbt describes the intermediate layer as “purpose-built transformation steps.” Further, “the best guiding principle is to think about verbs (e.g. pivotedaggregated_to_userjoinedfanned_out_by_quanityfunnel_created, etc.) in the intermediate layer.

The project’s intermediate layer consists of two models related to users. The sample TICKIT database lumps all users into a single table. However, for analytics purposes, different user personas might interest marketing teams, such as buyers, sellers, sellers who also buy, and non-buyers (users who have never purchased tickets). The two models in the project’s intermediate layer filter for buyers and for sellers, resulting in two separate views of user personas.

{{ config(materialized='view', bind=False) }}
with sales as (
select * from {{ ref('stg_tickit__sales') }}
),
users as (
select * from {{ ref('stg_tickit__users') }}
),
first_purchase as (
select min(date(sale_time)) as first_purchase_date, buyer_id
from sales
group by buyer_id
),
final as (
select distinct
u.user_id,
u.username,
cast((u.last_name||', '||u.first_name) as varchar(100)) as full_name,
f.first_purchase_date,
u.city,
u.state,
u.email,
u.phone,
u.like_broadway,
u.like_classical,
u.like_concerts,
u.like_jazz,
u.like_musicals,
u.like_opera,
u.like_rock,
u.like_sports,
u.like_theatre,
u.like_vegas
from
sales as s
join users as u on u.user_id = s.buyer_id
join first_purchase as f on f.buyer_id = s.buyer_id
order by
user_id
)
select * from final

To materialize the intermediate layer’s two data models into views, execute the command, dbt run --select intermediate.

The Intermediate layer’s data models successfully materialized in Redshift

Once the command completes, we should observe a total of nine views in Amazon Redshift demo database’s tickit_dbt schema — seven staging and two intermediate, identified with the int_ prefix.

Amazon Redshift Query Editor v2 Console

Marts Layer

In their best practices guide, dbt describes the marts layer as “business defined entities.” Further, “this is the layer where everything comes together and we start to arrange all of our atoms (staging models) and molecules (intermediate models) into full-fledged cells that have identity and purpose. We sometimes like to call this the entity layer or concept layer, to emphasize that all our marts are meant to represent a specific entity or concept at its unique grain.

The project’s marts layer consists of four data models across marketing and sales. The models are materialized as two dimension tables and two fact tables. Although it is common practice to describe and label these as traditional star schema dimension (dim_) or fact (fct_) tables, in reality, the fact tables in this demonstration are actually flat, de-normalized, wide tables. Wide tables generally have better analytics performance in a modern data warehouse, according to Fivetran and others.

{{ config(materialized='table', sort='sale_id', dist='sale_id') }}
with categories as (
select * from {{ ref('stg_tickit__categories') }}
),
dates as (
select * from {{ ref('stg_tickit__dates') }}
),
events as (
select * from {{ ref('stg_tickit__events') }}
),
listings as (
select * from {{ ref('stg_tickit__listings') }}
),
sales as (
select * from {{ ref('stg_tickit__sales') }}
),
sellers as (
select * from {{ ref('int_sellers_extracted_from_users') }}
),
buyers as (
select * from {{ ref('int_buyers_extracted_from_users') }}
),
event_categories as (
select
e.event_id,
e.event_name,
c.cat_group,
c.cat_name
from events as e
join categories as c on c.cat_id = e.cat_id
),
final as (
select
s.sale_id,
s.sale_time,
d.qtr,
ec.cat_group,
ec.cat_name,
ec.event_name,
b.username as buyer_username,
b.full_name as buyer_name,
b.state as buyer_state,
b.first_purchase_date as buyer_first_purchase_date,
se.username as seller_username,
se.full_name as seller_name,
se.state as seller_state,
se.first_sale_date as seller_first_sale_date,
s.ticket_price,
s.qty_sold,
s.price_paid,
s.commission_prcnt,
s.commission,
s.earnings
from
sales as s
join listings as l on l.list_id = s.list_id
join buyers as b on b.user_id = s.buyer_id
join sellers as se on se.user_id = s.seller_id
join event_categories as ec on ec.event_id = s.event_id
join dates as d on d.date_id = s.date_id
order by
sale_id
)
select * from final
view raw fct_sales.sql hosted with ❤ by GitHub

The marts layer’s models take various dependencies through joins on staging and intermediate models. The data model above, fct_sales, has dependencies on multiple staging and intermediate models.

Lineage graph (dependency graph) of a data model, displayed in dbt Cloud

To materialize the marts layer’s four data models into tables, execute the command, dbt run --select marts.

The Marts layer’s data models successfully materialized in Redshift as tables

Once the command completes, we should observe four tables and nine views in the Redshift demo database’s tickit_dbt schema. Note how the dbt model for fct_sales (shown above), with its Jinja templating and multiple CTEs have been compiled into the resulting table in Redshift, this is the real magic of dbt!

Amazon Redshift Query Editor v2 Console showing fct_sales table

At this point, all of the project’s models have been compiled and created in the Redshift demo database by dbt.

Analyses

The demonstration’s project also contains example analyses. dbt allows us to version control more analytical-oriented SQL files within our dbt project using the analyses functionality of dbt. These analyses do not fit the fairly opinionated dbt model definition. We can compile the analyses SQL file using the dbt compile command, then copy and paste the resulting SQL statements from the target/compiled/ subdirectory into our data warehouse’s query tool of choice.

Analysis shown in dbt Cloud interface
Compiled analysis SQL executed in Amazon Redshift

Project Documentation

Using the dbt docs generate command will automatically generate the project’s documentation website from the SQL and YAML files. Documentations can be generated and displayed from your dbt Cloud account or hosted locally.

Project’s documentation website

Testing

According to dbt, “Tests are assertions you make about your models and other resources in your dbt project (e.g. sources, seeds, and snapshots). When you run dbt test, dbt will tell you if each test in your project passes or fails.” The project contains over 50 tests, split between the _tickit__sources.yml file and individual tests in the test/ directory. Typical dbt tests check for non-null and unique values, values within an expected numeric range, and values from a known list of strings. Any SELECT statement written in SQL can be tested.

name: user
description: dimension table – TICKIT users
columns:
name: userid
data_type: int
description: primary key
tests:
unique
not_null
name: username
data_type: char(8)
tests:
unique
not_null
name: firstname
data_type: varchar(30)
tests:
not_null
Snippet of tests in the _tickit__sources.sql file
all prices paid for tickets should be a positive value of $1 or greater
there are no credits (negative values)
select price_paid
from {{ ref('stg_tickit__sales') }}
group by price_paid
having not (price_paid >= 1)

Execute the project’s tests using the dbt test command. We can execute individual tests using the --select optional argument, for example, dbt test --select assert_all_sale_amounts_are_positive. We can also use the --threads optional argument with most dbt commands, including dbt test, increasing parallelism and reducing execution time. The example below uses 10 threads, the arbitrary maximum configured for the Amazon Redshift dbt user.

Running dbt tests
Successful test run

Jobs

According to dbt, Jobs are a set of dbt commands that you want to run on a schedule. For example, dbt run and dbt test. Jobs can load packages, run tests, materialize models, check source freshness (dbt source freshness), and regenerate documentation. Below, we have created a daily job to test, refresh, and document our project as the data is updated in the data lake.

dbt Cloud’s Job Run Overview

Notifications

According to dbt, Setting up notifications in dbt Cloud will allow you to receive alerts via Email or a chosen Slack channel when a job run succeeds, fails, or is canceled.

dbt Cloud’s Notifications interface for configuring email and Slack

The Slack notifications include run status, timings, and a link to open the job in dbt Cloud. Below, we see a notification regarding our project’s daily job run.

Notification of successful job run in Slack from dbt Cloud

Exposures

Exposures are a recent addition to dbt. Exposures make it possible to define and describe a downstream use of our dbt project, such as in a dashboard, application, or data science pipeline. Below we see an example of an exposure describing a sales dashboard created in Amazon QuickSight.

version: 2
exposures:
name: tickit_sales_summary
type: dashboard
maturity: medium
url: https://us-east-1.quicksight.aws.amazon.com
description: >
TICKIT sales summary dashboard, authored in Amazon QuickSight
depends_on:
ref('fct_sales')
ref('fct_listings')
owner:
name: Gary A. Stafford
email: gary.a.stafford@gmail.com
view raw dashboard.yml hosted with ❤ by GitHub

The exposure YAML file shown above describes the Amazon QuickSight dashboard shown below.

Amazon QuickSight dashboard

Exposures work with dbt’s auto-documentation feature. dbt populates a dedicated page in the auto-generated documentation site with context relevant to data consumers.

Project’s documentation website, showing dashboard exposure
Project’s documentation website, showing dashboard exposure’s lineage graph

Conclusion

In this post, we covered some of the basic functionality of dbt. We learned how dbt enables analysts to work more like software engineers. We also learned how dbt makes it easy to codify data models in SQL, to version control and manage data models as code with git, and collaborate on data models with other data team members.

Topics not explored in this post but critical to most large-scale dbt-managed production environments include advanced Jinja templating and macrosmodel freshnessorchestrationjob schedulingContinuous Integration and GitOpsnotificationsenvironment variables, and incremental models. We will explore these additional dbt capabilities in future posts.


This blog represents my viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners. All diagrams and illustrations are the property of the author unless otherwise noted.

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Streaming Analytics with Data Warehouses, using Amazon Kinesis Data Firehose, Amazon Redshift, and Amazon QuickSight

Introduction

Databases are ideal for storing and organizing data that requires a high volume of transaction-oriented query processing while maintaining data integrity. In contrast, data warehouses are designed for performing data analytics on vast amounts of data from one or more disparate sources. In our fast-paced, hyper-connected world, those sources often take the form of continuous streams of web application logs, e-commerce transactions, social media feeds, online gaming activities, financial trading transactions, and IoT sensor readings. Streaming data must be analyzed in near real-time, while often first requiring cleansing, transformation, and enrichment.

In the following post, we will demonstrate the use of Amazon Kinesis Data Firehose, Amazon Redshift, and Amazon QuickSight to analyze streaming data. We will simulate time-series data, streaming from a set of IoT sensors to Kinesis Data Firehose. Kinesis Data Firehose will write the IoT data to an Amazon S3 Data Lake, where it will then be copied to Redshift in near real-time. In Amazon Redshift, we will enhance the streaming sensor data with data contained in the Redshift data warehouse, which has been gathered and denormalized into a star schema.

Streaming-Kinesis-Redshift

In Redshift, we can analyze the data, asking questions like, what is the min, max, mean, and median temperature over a given time period at each sensor location. Finally, we will use Amazon Quicksight to visualize the Redshift data using rich interactive charts and graphs, including displaying geospatial sensor data.

screen_shot_2020-03-04_at_9.27.33_pm

Featured Technologies

The following AWS services are discussed in this post.

Amazon Kinesis Data Firehose

According to Amazon, Amazon Kinesis Data Firehose can capture, transform, and load streaming data into data lakes, data stores, and analytics tools. Direct Kinesis Data Firehose integrations include Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. Kinesis Data Firehose enables near real-time analytics with existing business intelligence (BI) tools and dashboards.

Amazon Redshift

According to Amazon, Amazon Redshift is the most popular and fastest cloud data warehouse. With Redshift, users can query petabytes of structured and semi-structured data across your data warehouse and data lake using standard SQL. Redshift allows users to query and export data to and from data lakes. Redshift can federate queries of live data from Redshift, as well as across one or more relational databases.

Amazon Redshift Spectrum

According to Amazon, Amazon Redshift Spectrum can efficiently query and retrieve structured and semistructured data from files in Amazon S3 without having to load the data into Amazon Redshift tables. Redshift Spectrum tables are created by defining the structure for data files and registering them as tables in an external data catalog. The external data catalog can be AWS Glue or an Apache Hive metastore. While Redshift Spectrum is an alternative to copying the data into Redshift for analysis, we will not be using Redshift Spectrum in this post.

Amazon QuickSight

According to Amazon, Amazon QuickSight is a fully managed business intelligence service that makes it easy to deliver insights to everyone in an organization. QuickSight lets users easily create and publish rich, interactive dashboards that include Amazon QuickSight ML Insights. Dashboards can then be accessed from any device and embedded into applications, portals, and websites.

What is a Data Warehouse?

According to Amazon, a data warehouse is a central repository of information that can be analyzed to make better-informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. Business analysts, data scientists, and decision-makers access the data through business intelligence tools, SQL clients, and other analytics applications.

Demonstration

Source Code

All the source code for this post can be found on GitHub. Use the following command to git clone a local copy of the project.


git clone \
–branch master –single-branch –depth 1 –no-tags \
https://github.com/garystafford/kinesis-redshift-streaming-demo.git

CloudFormation

Use the two AWS CloudFormation templates, included in the project, to build two CloudFormation stacks. Please review the two templates and understand the costs of the resources before continuing.

The first CloudFormation template, redshift.yml, provisions a new Amazon VPC with associated network and security resources, a single-node Redshift cluster, and two S3 buckets.

The second CloudFormation template, kinesis-firehose.yml, provisions an Amazon Kinesis Data Firehose delivery stream, associated IAM Policy and Role, and an Amazon CloudWatch log group and two log streams.

Change the REDSHIFT_PASSWORD value to ensure your security. Optionally, change the REDSHIFT_USERNAME value. Make sure that the first stack completes successfully, before creating the second stack.


export AWS_DEFAULT_REGION=us-east-1
REDSHIFT_USERNAME=awsuser
REDSHIFT_PASSWORD=5up3r53cr3tPa55w0rd
# Create resources
aws cloudformation create-stack \
–stack-name redshift-stack \
–template-body file://cloudformation/redshift.yml \
–parameters ParameterKey=MasterUsername,ParameterValue=${REDSHIFT_USERNAME} \
ParameterKey=MasterUserPassword,ParameterValue=${REDSHIFT_PASSWORD} \
ParameterKey=InboundTraffic,ParameterValue=$(curl ifconfig.me -s)/32 \
–capabilities CAPABILITY_NAMED_IAM
# Wait for first stack to complete
aws cloudformation create-stack \
–stack-name kinesis-firehose-stack \
–template-body file://cloudformation/kinesis-firehose.yml \
–parameters ParameterKey=MasterUserPassword,ParameterValue=${REDSHIFT_PASSWORD} \
–capabilities CAPABILITY_NAMED_IAM

Review AWS Resources

To confirm all the AWS resources were created correctly, use the AWS Management Console.

Kinesis Data Firehose

In the Amazon Kinesis Dashboard, you should see the new Amazon Kinesis Data Firehose delivery stream, redshift-delivery-stream.

screen_shot_2020-03-02_at_3.58.12_pm

The Details tab of the new Amazon Kinesis Firehose delivery stream should look similar to the following. Note the IAM Role, FirehoseDeliveryRole, which was created and associated with the delivery stream by CloudFormation.

screen_shot_2020-03-02_at_6.30.25_pm

We are not performing any transformations of the incoming messages. Note the new S3 bucket that was created and associated with the stream by CloudFormation. The bucket name was randomly generated. This bucket is where the incoming messages will be written.

screen_shot_2020-03-02_at_6.31.13_pm

Note the buffer conditions of 1 MB and 60 seconds. Whenever the buffer of incoming messages is greater than 1 MB or the time exceeds 60 seconds, the messages are written in JSON format, using GZIP compression, to S3. These are the minimal buffer conditions, and as close to real-time streaming to Redshift as we can get.

screen_shot_2020-03-02_at_6.31.28_pm

Note the COPY command, which is used to copy the messages from S3 to the message table in Amazon Redshift. Kinesis uses the IAM Role, ClusterPermissionsRole, created by CloudFormation, for credentials. We are using a Manifest to copy the data to Redshift from S3. According to Amazon, a Manifest ensures that the COPY command loads all of the required files, and only the required files, for a data load. The Manifests are automatically generated and managed by the Kinesis Firehose delivery stream.

screen_shot_2020-03-02_at_6.31.43_pm

Redshift Cluster

In the Amazon Redshift Console, you should see a new single-node Redshift cluster consisting of one Redshift dc2.large Dense Compute node type.

screen_shot_2020-03-02_at_7.09.35_pm

Note the new VPC, Subnet, and VPC Security Group created by CloudFormation. Also, observe that the Redshift cluster is publicly accessible from outside the new VPC.

screen_shot_2020-03-02_at_7.09.41_pm

Redshift Ingress Rules

The single-node Redshift cluster is assigned to an AWS Availability Zone in the US East (N. Virginia) us-east-1 AWS Region. The cluster is associated with a VPC Security Group. The Security Group contains three inbound rules, all for Redshift port 5439. The IP addresses associated with the three inbound rules provide access to the following: 1) a /27 CIDR block for Amazon QuickSight in us-east-1, a /27 CIDR block for Amazon Kinesis Firehose in us-east-1, and to you, a /32 CIDR block with your current IP address. If your IP address changes or you do not use the us-east-1 Region, you will need to change one or all of these IP addresses. The list of Kinesis Firehose IP addresses is here. The list of QuickSight IP addresses is here.

screen_shot_2020-03-02_at_7.09.59_pm

If you cannot connect to Redshift from your local SQL client, most often, your IP address has changed and is incorrect in the Security Group’s inbound rule.

Redshift SQL Client

You can choose to use the Redshift Query Editor to interact with Redshift or use a third-party SQL client for greater flexibility. To access the Redshift Query Editor, use the user credentials specified in the redshift.yml CloudFormation template.

screen_shot_2020-03-02_at_4.01.49_pm

There is a lot of useful functionality in the Redshift Console and within the Redshift Query Editor. However, a notable limitation of the Redshift Query Editor, in my opinion, is the inability to execute multiple SQL statements at the same time. Whereas, most SQL clients allow multiple SQL queries to be executed at the same time.

screen_shot_2020-03-04_at_8.48.39_am

I prefer to use JetBrains PyCharm IDE. PyCharm has out-of-the-box integration with Redshift. Using PyCharm, I can edit the project’s Python, SQL, AWS CLI shell, and CloudFormation code, all from within PyCharm.

screen_shot_2020-03-02_at_7.12.11_pm

If you use any of the common SQL clients, you will need to set-up a JDBC (Java Database Connectivity) or ODBC (Open Database Connectivity) connection to Redshift. The ODBC and JDBC connection strings can be found in the Redshift cluster’s Properties tab or in the Outputs tab from the CloudFormation stack, redshift-stack.

screen_shot_2020-03-04_at_9.07.14_am

You will also need the Redshift database username and password you included in the aws cloudformation create-stack AWS CLI command you executed previously. Below, we see PyCharm’s Project Data Sources window containing a new data source for the Redshift dev database.

screen_shot_2020-03-02_at_6.33.01_pm

Database Schema and Tables

When CloudFormation created the Redshift cluster, it also created a new database, dev. Using the Redshift Query Editor or your SQL client of choice, execute the following series of SQL commands to create a new database schema, sensor, and six tables in the sensor schema.


Create new schema in Redshift DB
DROP SCHEMA IF EXISTS sensor CASCADE;
CREATE SCHEMA sensor;
SET search_path = sensor;
Create (6) tables in Redshift DB
CREATE TABLE message streaming data table
(
id BIGINT IDENTITY (1, 1), message id
guid VARCHAR(36) NOT NULL, device guid
ts BIGINT NOT NULL DISTKEY SORTKEY, epoch in seconds
temp NUMERIC(5, 2) NOT NULL, temperature reading
created TIMESTAMP DEFAULT ('now'::text)::timestamp with time zone row created at
);
CREATE TABLE location dimension table
(
id INTEGER NOT NULL DISTKEY SORTKEY, location id
long NUMERIC(10, 7) NOT NULL, longitude
lat NUMERIC(10, 7) NOT NULL, latitude
description VARCHAR(256) location description
);
CREATE TABLE history dimension table
(
id INTEGER NOT NULL DISTKEY SORTKEY, history id
serviced BIGINT NOT NULL, service date
action VARCHAR(20) NOT NULL, INSTALLED, CALIBRATED, FIRMWARE UPGRADED, DECOMMISSIONED, OTHER
technician_id INTEGER NOT NULL, technician id
notes VARCHAR(256) notes
);
CREATE TABLE sensor dimension table
(
id INTEGER NOT NULL DISTKEY SORTKEY, sensor id
guid VARCHAR(36) NOT NULL, device guid
mac VARCHAR(18) NOT NULL, mac address
sku VARCHAR(18) NOT NULL, product sku
upc VARCHAR(12) NOT NULL, product upc
active BOOLEAN DEFAULT TRUE, active status
notes VARCHAR(256) notes
);
CREATE TABLE manufacturer dimension table
(
id INTEGER NOT NULL DISTKEY SORTKEY, manufacturer id
name VARCHAR(100) NOT NULL, company name
website VARCHAR(100) NOT NULL, company website
notes VARCHAR(256) notes
);
CREATE TABLE sensors fact table
(
id BIGINT IDENTITY (1, 1) DISTKEY SORTKEY, fact id
sensor_id INTEGER NOT NULL, sensor id
manufacturer_id INTEGER NOT NULL, manufacturer id
location_id INTEGER NOT NULL, location id
history_id BIGINT NOT NULL, history id
message_guid VARCHAR(36) NOT NULL sensor guid
);

Star Schema

The tables represent denormalized data, taken from one or more relational database sources. The tables form a star schema.  The star schema is widely used to develop data warehouses. The star schema consists of one or more fact tables referencing any number of dimension tables. The location, manufacturer, sensor, and history tables are dimension tables. The sensors table is a fact table.

In the diagram below, the foreign key relationships are virtual, not physical. The diagram was created using PyCharm’s schema visualization tool. Note the schema’s star shape. The message table is where the streaming IoT data will eventually be written. The message table is related to the sensors fact table through the common guid field.

schema-light-2

Sample Data to S3

Next, copy the sample data, included in the project, to the S3 data bucket created with CloudFormation. Each CSV-formatted data file corresponds to one of the tables we previously created. Since the bucket name is semi-random, we can use the AWS CLI and jq to get the bucket name, then use it to perform the copy commands.


# Get data bucket name
DATA_BUCKET=$(aws cloudformation describe-stacks \
–stack-name redshift-stack \
| jq -r '.Stacks[].Outputs[] | select(.OutputKey == "DataBucket") | .OutputValue')
echo $DATA_BUCKET
# Copy data
aws s3 cp data/history.csv s3://${DATA_BUCKET}/history/history.csv
aws s3 cp data/location.csv s3://${DATA_BUCKET}/location/location.csv
aws s3 cp data/manufacturer.csv s3://${DATA_BUCKET}/manufacturer/manufacturer.csv
aws s3 cp data/sensor.csv s3://${DATA_BUCKET}/sensor/sensor.csv
aws s3 cp data/sensors.csv s3://${DATA_BUCKET}/sensors/sensors.csv

The output from the AWS CLI should look similar to the following.

screen_shot_2020-03-02_at_7.18.22_pm

Sample Data to Redshift

Whereas a relational database, such as Amazon RDS is designed for online transaction processing (OLTP), Amazon Redshift is designed for online analytic processing (OLAP) and business intelligence applications. To write data to Redshift we typically use the COPY command versus frequent, individual INSERT statements, as with OLTP, which would be prohibitively slow. According to Amazon, the Redshift COPY command leverages the Amazon Redshift massively parallel processing (MPP) architecture to read and load data in parallel from files on Amazon S3, from a DynamoDB table, or from text output from one or more remote hosts.

In the following series of SQL statements, replace the placeholder, your_bucket_name, in five places with your S3 data bucket name. The bucket name will start with the prefix, redshift-stack-databucket. The bucket name can be found in the Outputs tab of the CloudFormation stack, redshift-stack. Next, replace the placeholder, cluster_permissions_role_arn, with the ARN (Amazon Resource Name) of the ClusterPermissionsRole. The ARN is formatted as follows, arn:aws:iam::your-account-id:role/ClusterPermissionsRole. The ARN can be found in the Outputs tab of the CloudFormation stack, redshift-stack.

Using the Redshift Query Editor or your SQL client of choice, execute the SQL statements to copy the sample data from S3 to each of the corresponding tables in the Redshift dev database. The TRUNCATE commands guarantee there is no previous sample data present in the tables.


** MUST FIRST CHANGE your_bucket_name and cluster_permissions_role_arn **
sensor schema
SET search_path = sensor;
Copy sample data to tables from S3
TRUNCATE TABLE history;
COPY history (id, serviced, action, technician_id, notes)
FROM 's3://your_bucket_name/history/'
CREDENTIALS 'aws_iam_role=cluster_permissions_role_arn'
CSV IGNOREHEADER 1;
TRUNCATE TABLE location;
COPY location (id, long, lat, description)
FROM 's3://your_bucket_name/location/'
CREDENTIALS 'aws_iam_role=cluster_permissions_role_arn'
CSV IGNOREHEADER 1;
TRUNCATE TABLE sensor;
COPY sensor (id, guid, mac, sku, upc, active, notes)
FROM 's3://your_bucket_name/sensor/'
CREDENTIALS 'aws_iam_role=cluster_permissions_role_arn'
CSV IGNOREHEADER 1;
TRUNCATE TABLE manufacturer;
COPY manufacturer (id, name, website, notes)
FROM 's3://your_bucket_name/manufacturer/'
CREDENTIALS 'aws_iam_role=cluster_permissions_role_arn'
CSV IGNOREHEADER 1;
TRUNCATE TABLE sensors;
COPY sensors (sensor_id, manufacturer_id, location_id, history_id, message_guid)
FROM 's3://your_bucket_name/sensors/'
CREDENTIALS 'aws_iam_role=cluster_permissions_role_arn'
CSV IGNOREHEADER 1;
SELECT COUNT(*) FROM history; 30
SELECT COUNT(*) FROM location; 6
SELECT COUNT(*) FROM sensor; 6
SELECT COUNT(*) FROM manufacturer; 1
SELECT COUNT(*) FROM sensors; 30

Database Views

Next, create four Redshift database Views. These views may be used to analyze the data in Redshift, and later, in Amazon QuickSight.

  1. sensor_msg_detail: Returns aggregated sensor details, using the sensors fact table and all five dimension tables in a SQL Join.
  2. sensor_msg_count: Returns the number of messages received by Redshift, for each sensor.
  3. sensor_avg_temp: Returns the average temperature from each sensor, based on all the messages received from each sensor.
  4. sensor_avg_temp_current: View is identical for the previous view but limited to the last 30 minutes.

Using the Redshift Query Editor or your SQL client of choice, execute the following series of SQL statements.


sensor schema
SET search_path = sensor;
View 1: Sensor details
DROP VIEW IF EXISTS sensor_msg_detail;
CREATE OR REPLACE VIEW sensor_msg_detail AS
SELECT ('1970-01-01'::date + e.ts * interval '1 second') AS recorded,
e.temp,
s.guid,
s.sku,
s.mac,
l.lat,
l.long,
l.description AS location,
('1970-01-01'::date + h.serviced * interval '1 second') AS installed,
e.created AS redshift
FROM sensors f
INNER JOIN sensor s ON (f.sensor_id = s.id)
INNER JOIN history h ON (f.history_id = h.id)
INNER JOIN location l ON (f.location_id = l.id)
INNER JOIN manufacturer m ON (f.manufacturer_id = m.id)
INNER JOIN message e ON (f.message_guid = e.guid)
WHERE s.active IS TRUE
AND h.action = 'INSTALLED'
ORDER BY f.id;
View 2: Message count per sensor
DROP VIEW IF EXISTS sensor_msg_count;
CREATE OR REPLACE VIEW sensor_msg_count AS
SELECT count(e.temp) AS msg_count,
s.guid,
l.lat,
l.long,
l.description AS location
FROM sensors f
INNER JOIN sensor s ON (f.sensor_id = s.id)
INNER JOIN history h ON (f.history_id = h.id)
INNER JOIN location l ON (f.location_id = l.id)
INNER JOIN message e ON (f.message_guid = e.guid)
WHERE s.active IS TRUE
AND h.action = 'INSTALLED'
GROUP BY s.guid, l.description, l.lat, l.long
ORDER BY msg_count, s.guid;
View 3: Average temperature per sensor (all data)
DROP VIEW IF EXISTS sensor_avg_temp;
CREATE OR REPLACE VIEW sensor_avg_temp AS
SELECT avg(e.temp) AS avg_temp,
count(s.guid) AS msg_count,
s.guid,
l.lat,
l.long,
l.description AS location
FROM sensors f
INNER JOIN sensor s ON (f.sensor_id = s.id)
INNER JOIN history h ON (f.history_id = h.id)
INNER JOIN location l ON (f.location_id = l.id)
INNER JOIN message e ON (f.message_guid = e.guid)
WHERE s.active IS TRUE
AND h.action = 'INSTALLED'
GROUP BY s.guid, l.description, l.lat, l.long
ORDER BY avg_temp, s.guid;
View 4: Average temperature per sensor (last 30 minutes)
DROP VIEW IF EXISTS sensor_avg_temp_current;
CREATE OR REPLACE VIEW sensor_avg_temp_current AS
SELECT avg(e.temp) AS avg_temp,
count(s.guid) AS msg_count,
s.guid,
l.lat,
l.long,
l.description AS location
FROM sensors f
INNER JOIN sensor s ON (f.sensor_id = s.id)
INNER JOIN history h ON (f.history_id = h.id)
INNER JOIN location l ON (f.location_id = l.id)
INNER JOIN (SELECT ('1970-01-01'::date + ts * interval '1 second') AS recorded_time,
guid,
temp
FROM message
WHERE DATEDIFF(minute, recorded_time, GETDATE()) <= 30) e ON (f.message_guid = e.guid)
WHERE s.active IS TRUE
AND h.action = 'INSTALLED'
GROUP BY s.guid, l.description, l.lat, l.long
ORDER BY avg_temp, s.guid;

At this point, you should have a total of six tables and four views in the sensor schema of the dev database in Redshift.

Test the System

With all the necessary AWS resources and Redshift database objects created and sample data in the Redshift database, we can test the system. The included Python script, kinesis_put_test_msg.py, will generate a single test message and send it to Kinesis Data Firehose. If everything is working, the message should be delivered from Kinesis Data Firehose to S3, then copied to Redshift, and appear in the message table.

Install the required Python packages and then execute the Python script.


# Install required Python packages
python3 -m pip install –user -r scripts/requirements.txt
# Set default AWS Region for script
export AWS_DEFAULT_REGION=us-east-1
# Execute script in foreground
python3 ./scripts/kinesis_put_test_msg.py

Run the following SQL query to confirm the record is in the message table of the dev database. It will take at least one minute for the message to appear in Redshift.


SELECT COUNT(*) FROM message;

Once the message is confirmed to be present in the message table, delete the record by truncating the table.


TRUNCATE TABLE message;

Streaming Data

Assuming the test message worked, we can proceed with simulating the streaming IoT sensor data. The included Python script, kinesis_put_streaming_data.py, creates six concurrent threads, representing six temperature sensors.


#!/usr/bin/env python3
# Simulated multiple streaming time-series iot sensor data
# Author: Gary A. Stafford
# Date: Revised October 2020
import json
import random
from datetime import datetime
import boto3
import time as tm
import numpy as np
import threading
STREAM_NAME = 'redshift-delivery-stream'
client = boto3.client('firehose')
class MyThread(threading.Thread):
def __init__(self, thread_id, sensor_guid, temp_max):
threading.Thread.__init__(self)
self.thread_id = thread_id
self.sensor_id = sensor_guid
self.temp_max = temp_max
def run(self):
print("Starting Thread: " + str(self.thread_id))
self.create_data()
print("Exiting Thread: " + str(self.thread_id))
def create_data(self):
start = 0
stop = 20
step = 0.1 # step size (e.g 0 to 20, step .1 = 200 steps in cycle)
repeat = 2 # how many times to repeat cycle
freq = 60 # frequency of temperature reading in seconds
max_range = int(stop * (1 / step))
time = np.arange(start, stop, step)
amplitude = np.sin(time)
for x in range(0, repeat):
for y in range(0, max_range):
temperature = round((((amplitude[y] + 1.0) * self.temp_max) + random.uniform(5, 5)) + 60, 2)
payload = {
'guid': self.sensor_id,
'ts': int(datetime.now().strftime('%s')),
'temp': temperature
}
print(json.dumps(payload))
self.send_to_kinesis(payload)
tm.sleep(freq)
@staticmethod
def send_to_kinesis(payload):
_ = client.put_record(
DeliveryStreamName=STREAM_NAME,
Record={
'Data': json.dumps(payload)
}
)
def main():
sensor_guids = [
"03e39872-e105-4be4-83c0-9ade818465dc",
"fa565921-fddd-4bfb-a7fd-d617f816df4b",
"d120422d-5789-435d-9dc6-73d8489b04c2",
"93238559-4d55-4b2a-bdcb-6aa3be0f3908",
"dbc05806-6872-4f0a-aca2-f794cc39bd9b",
"f9ade639-f936-4954-aa5a-1f2ed86c9bcf"
]
timeout = 300 # arbitrarily offset the start of threads (60 / 5 = 12)
# Create new threads
thread1 = MyThread(1, sensor_guids[0], 25)
thread2 = MyThread(2, sensor_guids[1], 10)
thread3 = MyThread(3, sensor_guids[2], 7)
thread4 = MyThread(4, sensor_guids[3], 30)
thread5 = MyThread(5, sensor_guids[4], 5)
thread6 = MyThread(6, sensor_guids[5], 12)
# Start new threads
thread1.start()
tm.sleep(timeout * 1)
thread2.start()
tm.sleep(timeout * 2)
thread3.start()
tm.sleep(timeout * 1)
thread4.start()
tm.sleep(timeout * 3)
thread5.start()
tm.sleep(timeout * 2)
thread6.start()
# Wait for threads to terminate
thread1.join()
thread2.join()
thread3.join()
thread4.join()
thread5.join()
thread6.join()
print("Exiting Main Thread")
if __name__ == '__main__':
main()

The simulated data uses an algorithm that follows an oscillating sine wave or sinusoid, representing rising and falling temperatures. In the script, I have configured each thread to start with an arbitrary offset to add some randomness to the simulated data.

screen_shot_2020-03-04_at_9.27.33_pm

The variables within the script can be adjusted to shorten or lengthen the time it takes to stream the simulated data. By default, each of the six threads creates 400 messages per sensor, in one-minute increments. Including the offset start of each proceeding thread, the total runtime of the script is about 7.5 hours to generate 2,400 simulated IoT sensor temperature readings and push to Kinesis Data Firehose. Make sure you can guarantee you will maintain a connection to the Internet for the entire runtime of the script. I normally run the script in the background, from a small EC2 instance.

To use the Python script, execute either of the two following commands. Using the first command will run the script in the foreground. Using the second command will run the script in the background.


# Install required Python packages
python3 -m pip install –user -r scripts/requirements.txt
# Set default AWS Region for script
export AWS_DEFAULT_REGION=us-east-1
# Option #1: Execute script in foreground
python3 ./scripts/kinesis_put_streaming_data.py
# Option #2: execute script in background
nohup python3 -u ./scripts/kinesis_put_streaming_data.py > output.log 2>&1 </dev/null &
# Check that the process is running
ps -aux | grep 'python3 -u ./scripts/kinesis_put_streaming_data.py'
# Wait 1-2 minutes, then check output to confirm script is working
cat output.log

Viewing the output.log file, you should see messages being generated on each thread and sent to Kinesis Data Firehose. Each message contains the GUID of the sensor, a timestamp, and a temperature reading.

Screen Shot 2020-10-14 at 10.00.37 AM

The messages are sent to Kinesis Data Firehose, which in turn writes the messages to S3. The messages are written in JSON format using GZIP compression. Below, we see an example of the GZIP compressed JSON files in S3. The JSON files are partitioned by year, month, day, and hour.

screen_shot_2020-03-04_at_11.04.36_pm

Confirm Data Streaming to Redshift

From the Amazon Kinesis Firehose Console Metrics tab, you should see incoming messages flowing to S3 and on to Redshift.

screen_shot_2020-03-03_at_6.17.14_pm

Executing the following SQL query should show an increasing number of messages.


SELECT COUNT(*) FROM message;

How Near Real-time?

Earlier, we saw how the Amazon Kinesis Data Firehose delivery stream was configured to buffer data at the rate of 1 MB or 60 seconds. Whenever the buffer of incoming messages is greater than 1 MB or the time exceeds 60 seconds, the messages are written to S3. Each record in the message table has two timestamps. The first timestamp, ts, is when the temperature reading was recorded. The second timestamp, created, is when the message was written to Redshift, using the COPY command. We can calculate the delta in seconds between the two timestamps using the following SQL query in Redshift.


SELECT ('1970-01-01'::date + ts * interval '1 second') AS recorded_time,
created AS redshift_time,
DATEDIFF(seconds, recorded_time, redshift_time) AS diff_seconds
FROM message
ORDER BY diff_seconds;

Using the results of the Redshift query, we can visualize the results in Amazon QuickSight. In my own tests, we see that for 2,400 messages, over approximately 7.5 hours, the minimum delay was 1 second, and a maximum delay was 64 seconds. Hence, near real-time, in this case, is about one minute or less, with an average latency of roughly 30 seconds.

latency

Analyzing the Data with Redshift

I suggest waiting at least thirty minutes for a significant number of messages copied into Redshift. With the data streaming into Redshift, execute each of the database views we created earlier. You should see the streaming message data, joined to the existing static data in Redshift. As data continues to stream into Redshift, the views will display different results based on the current message table contents.

Here, we see the first ten results of the sensor_msg_detail view.



recorded temp guid sku mac lat long location installed redshift
2020-03-04 03:31:59.000000 105.56 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:33:01.580147
2020-03-04 03:29:59.000000 95.93 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:31:01.388887
2020-03-04 03:26:58.000000 91.93 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:28:01.099796
2020-03-04 03:25:58.000000 88.70 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:26:00.196113
2020-03-04 03:22:58.000000 87.65 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:23:01.558514
2020-03-04 03:20:58.000000 77.35 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:21:00.691347
2020-03-04 03:16:57.000000 71.84 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:17:59.307510
2020-03-04 03:15:57.000000 72.35 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:15:59.813656
2020-03-04 03:14:57.000000 67.95 03e39872-e105-4be4-83c0-9ade818465dc PR49-24A 8e:fa:46:09:14:b2 37.7068476 -122.4191599 Research Lab #2203 2018-01-31 12:00:00.000000 2020-03-04 03:15:59.813656

Next, we see the results of the sensor_avg_temp view.



avg_temp guid lat long location
65.25 dbc05806-6872-4f0a-aca2-f794cc39bd9b 37.7066541 -122.4181399 Wafer Inspection Lab #0210A
67.23 d120422d-5789-435d-9dc6-73d8489b04c2 37.7072686 -122.4187016 Zone 4 Wafer Processing Area B3
70.23 fa565921-fddd-4bfb-a7fd-d617f816df4b 37.7071763 -122.4190397 Research Lab #2209
72.22 f9ade639-f936-4954-aa5a-1f2ed86c9bcf 37.7067618 -122.4186191 Wafer Inspection Lab #0211C
85.48 03e39872-e105-4be4-83c0-9ade818465dc 37.7068476 -122.4191599 Research Lab #2203
90.69 93238559-4d55-4b2a-bdcb-6aa3be0f3908 37.7070334 -122.4184393 Zone 2 Semiconductor Assembly Area A2

Amazon QuickSight

In a recent post, Getting Started with Data Analysis on AWS using AWS Glue, Amazon Athena, and QuickSight: Part 2, I detailed getting started with Amazon QuickSight. In this post, I will assume you are familiar with QuickSight.

Amazon recently added a full set of aws quicksight APIs for interacting with QuickSight. Though, for this part of the demonstration, we will be working directly in the Amazon QuickSight Console, as opposed to the AWS CLI, AWS CDK, or CloudFormation.

Redshift Data Sets

To visualize the data from Amazon Redshift, we start by creating Data Sets in QuickSight. QuickSight supports a large number of data sources for creating data sets. We will use the Redshift data source. If you recall, we added an inbound rule for QuickSight, allowing us to connect to our Redshift cluster in us-east-1.

screen_shot_2020-03-02_at_10.21.35_pm

We will select the sensor schema, which is where the tables and views for this demonstration are located.

screen_shot_2020-03-02_at_10.21.49_pm

We can choose any of the tables or views in the Redshift dev database that we want to use for visualization.

screen_shot_2020-03-02_at_10.22.11_pm

Below, we see examples of two new data sets, shown in the QuickSight Data Prep Console. Note how QuickSight automatically recognizes field types, including dates, latitude, and longitude.

screen_shot_2020-03-02_at_10.23.12_pm

screen_shot_2020-03-02_at_10.59.32_pm

Visualizations

Using the data sets, QuickSight allows us to create a wide number of rich visualizations. Below, we see the simulated time-series data from the six temperature sensors.

screen_shot_2020-03-04_at_9.26.26_pm

Next, we see an example of QuickSight’s ability to show geospatial data. The Map shows the location of each sensor and the average temperature recorded by that sensor.

screen_shot_2020-03-04_at_9.26.51_pm

Cleaning Up

To remove the resources created for this post, use the following series of AWS CLI commands.


# Get data bucket name
DATA_BUCKET=$(aws cloudformation describe-stacks \
–stack-name redshift-stack \
| jq -r '.Stacks[].Outputs[] | select(.OutputKey == "DataBucket") | .OutputValue')
echo ${DATA_BUCKET}
# Get log bucket name
LOG_BUCKET=$(aws cloudformation describe-stacks \
–stack-name redshift-stack \
| jq -r '.Stacks[].Outputs[] | select(.OutputKey == "LogBucket") | .OutputValue')
echo ${LOG_BUCKET}
# Delete demonstration resources
python3 ./scripts/delete_buckets.py
aws cloudformation delete-stack –stack-name kinesis-firehose-stack
# Wait for first stack to be deleted
aws cloudformation delete-stack –stack-name redshift-stack

Conclusion

In this brief post, we have learned how streaming data can be analyzed in near real-time, in Amazon Redshift, using Amazon Kinesis Data Firehose. Further, we explored how the results of those analyses can be visualized in Amazon QuickSight. For customers that depend on a data warehouse for data analytics, but who also have streaming data sources, the use of Amazon Kinesis Data Firehose or Amazon Redshift Spectrum is an excellent choice.

This blog represents my own viewpoints and not of my employer, Amazon Web Services.

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