Data Engineer with AWS Redshift Salary in 2024

Where is AWS Redshift used?

Big Data Party Warehouse

• Transforms data lakes into a disco ball of insights, shaking up analytics faster than you can say 'query.'

Analytics Time Machine

• Like Doc Brown, it zooms through historical data trends faster than a DeLorean hitting 88 MPH.

The Marketing Crystal Ball

• Peers into customer behaviors, predicting the next shopping spree like a fortune teller at a carnival.

Financial Puzzle Solver

• Lays out your dollars and cents like a Sudoku master, making your accounts as balanced as a zen monk.

AWS Redshift Alternatives

Google BigQuery

BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service (PaaS) that supports SQL and automatic data encryption.

SELECT name, COUNT(*) as name_count
FROM `bigquery-public-data.usa_names.usa_1910_current`
GROUP BY name
ORDER BY name_count DESC
LIMIT 10

• Serverless, no infrastructure to manage

• Real-time analytics with high-speed streaming insert

• Integrates with Google's data ecosystem

• Query pricing may be unpredictable

• Lack of control over performance tweaks

• Vendor lock-in specific to Google’s ecosystem

Microsoft Azure Synapse Analytics

Azure Synapse is an analytics service that brings together enterprise data warehousing and Big Data analytics. It offers a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

SELECT TOP 10 *
FROM [SalesLT].[Product]
WHERE Color = 'Black'

• Tightly integrated with other Azure services

• On-demand or provisioned resources

• Powerful security features

• Can be complex to set up and manage

• Potential for higher costs with scaling

• Less ideal for organizations not committed to Azure

Snowflake

Snowflake is a cloud-based data platform built for the cloud that supports a wide range of technology ecosystems. It offers near-unlimited scale, concurrency, and performance.

SELECT COUNT(*)
FROM database.schema.table;

• Supports multi-cloud environments

• Separate compute and storage scaling

• Simple to use with a clear pricing model

• Data transfer costs between clouds

• Extra cost for advanced features

• Required data loading and handling

Quick Facts about AWS Redshift

Redshift: AWS's Data Warehouse Powerhouse

Picture this: the year is 2012, and the cloud is bursting with potential. Amazon Web Services busts onto the scene with Redshift, and suddenly, big data analytics is accessible to even the smallest of businesses. This petabyte-scale data warehousing service isn't just a storage hub; it's the Usain Bolt of data queries, racing through massive datasets faster than a squirrel on espresso.

Columnar Storage Shenanigans

AWS Redshift flipped the script on data storage by ditching the old-school row-based storage for columnar storage, making data analysts practically giddy with speed improvements. Imagine trading in your bulky filing cabinet for a sleek, streamlined set of binders. Each query is like a ninja slicing through data, only grabbing what it needs – a sheer act of technical artistry.

Continuous Ingenuity With Spectrum

In the twisty-turny world of tech, AWS Redshift kept spicing things up, rolling out Redshift Spectrum in 2017. With Spectrum, your querying game steps up to a whole new league, scouring through exabytes of data in S3 with no sweat. Now that's like having a superpowered magnifying glass that can spot an ant from the top of the Empire State Building!

What is the difference between Junior, Middle, Senior and Expert AWS Redshift developer?

Top 10 AWS Redshift Related Tech

1. 
   

   SQL (Structured Query Language)

   
   

   Behold SQL, the mighty gatekeeper to the world of Redshift data! It’s like the magic words that unlock the treasures within your database - a must-know lingo for wooing the rows and columns. From SELECT statements that play favorites by picking specific data, to INSERT spells that let new data crash the party, SQL is the grandmaster of data manipulation in Redshift’s relational database dojo.

   
   

   
   SELECT customer_id, SUM(order_total)
   FROM sales
   GROUP BY customer_id;

1. 
   

   Python

   
   

   Python slithers into Redshift development like a nimble ninja, blending seamlessly with its psycopg2 and SQLalchemy libraries. Whisper an API incantation or craft a data pipelining charm, and behold as rows and columns dance at your command. With Python, you're the puppeteer of petabytes, orchestrating ETL symphonies and analytics ballets with ease.

   
   

   
   import psycopg2
   
   connection = psycopg2.connect(
       dbname='your_db', 
       user='you', 
       password='supersecret', 
       host='your-redshift-cluster'
   )
   # Dance, data, dance!

1. 
   

   Amazon S3 (Simple Storage Service)

   
   

   Imagine a boundless chest where your troves of data pirates stash their treasures – that’s S3 for Redshift. It’s the trusty sidekick, dutifully securing your booty (data) in digital lockers until Redshift beckons with COPY commands. Like a well-oiled switchboard, it operates round-the-clock, ensuring swift, seamless pours of data into Redshift’s voracious maw.

   
   

   
   COPY sales
   FROM 's3://your-bucket/sales/'
   CREDENTIALS 'aws_iam_role=your-iam-role'
   CSV;

1. 
   

   AWS Data Pipeline

   
   

   Picture a bustling factory line neatly arranged within the cloud, that’s AWS Data Pipeline for you. It’s the conveyor belt that plays matchmaker between disparate data sources and AWS services. Automate this Romeo and Juliet of data flows, and you’ll see star-crossed datasets unite within Redshift's embrace, dancing a tango of synchronized updates and orchestrated loads.

1. 
   

   AWS Lambda

   
   

   When you wish to add a dash of wizardry to your Redshift escapades, Lambda is the enchanting wand. Cast a serverless incantation to conjure data transformations or mystical event responses. It’s your loyal spellbook, brimming with scripts that zap into action on a whim, manipulating your data lakes with a flick and a function.

   
   

   
   exports.handler = async (event) => {
       // Your Lambda magic here.
   };

1. 
   

   Apache Spark

   
   

   Dive into the cauldron of big data sorcery with Apache Spark, the alchemist’s stone turning raw data into golden insights. With the Spark-Redshift concoction, you can distill rivers of data into potent elixirs of analysis, incorporating Python or Scala spells for that extra kick of speed and power. It's like brewing an analytics potion with the intensity cranked to eleven.

1. 
   

   Tableau

   
   

   Step right up and gaze into Tableau’s crystalline orbs, wherein lies the power to visualize Redshift’s prophecies. Through the mystic arts of drag-and-drop, behold as data points leap into vivid charts and graphs. With Tableau's visionary prowess, even the murkiest of Redshift datasets unravel into tapestries of insight that mere mortals can behold and understand.

1. 
   

   Amazon QuickSight

   
   

   In the realm of business intelligence, Amazon QuickSight emerges as your crystal ball into the future. It peers directly into the soul of Redshift, unveiling the hidden stories within your data. With blazing scrolls (dashboards) and encrypted runes (analyses), it brings forth clarity from chaos, all with the swiftness of a well-aimed arrow.

1. 
   

   Amazon Glue

   
   

   When your data feels as scattered as a jester’s thoughts, Amazon Glue sticks the pieces together with the finesse of a master craftsman. It's the dungeon keeper of metadata, the ETL alchemist that whispers sweet nothings to disparate sources making them seamlessly assimilate into Redshift’s vaulted halls, ready for querying knights to explore.

1. 
   

   Terraform

   
   

   In the kingdom of Redshift, Terraform carves the very earth beneath your feet. It lays the infrastructure like a masterful mage casting a grand spell, conjuring servers and storage from the nether with the mere utterance of a ‘plan’ and ‘apply’. Invoke its power responsibly, for with great infrastructure-as-code comes great efficiency.

   
   

   
   resource "aws_redshift_cluster" "default" {
     cluster_identifier = "tf-redshift-cluster"
     database_name      = "mydb"
     node_type          = "dc2.large"
     number_of_nodes    = 1
   }