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  1. Stackups
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  4. Big Data As A Service
  5. AWS Lambda vs Amazon Redshift

AWS Lambda vs Amazon Redshift

OverviewDecisionsComparisonAlternatives

Overview

Amazon Redshift
Amazon Redshift
Stacks1.5K
Followers1.4K
Votes108
AWS Lambda
AWS Lambda
Stacks26.0K
Followers18.8K
Votes432

AWS Lambda vs Amazon Redshift: What are the differences?

Introduction

AWS Lambda and Amazon Redshift are both services offered by Amazon Web Services (AWS) and are commonly used in data processing and analytics. However, they have some key differences.

  1. Scalability: AWS Lambda is designed to automatically scale based on the incoming requests. It can handle a high volume of requests concurrently and can scale out to multiple instances. On the other hand, Amazon Redshift is designed for parallel data processing, with the ability to distribute and partition data across multiple nodes for parallel querying and faster performance.

  2. Data Processing: AWS Lambda is primarily used for executing small, stateless functions in response to events. It is commonly used for tasks such as data transformations, real-time file processing, and event-driven processing. On the other hand, Amazon Redshift is a fully-managed data warehousing solution optimized for processing large volumes of structured, semi-structured, and unstructured data. It is commonly used for data analytics, business intelligence, and reporting.

  3. Storage: AWS Lambda does not provide persistent storage. It is designed to be stateless, and any required input/output data can be stored or retrieved from other AWS services, such as Amazon S3 or DynamoDB. On the other hand, Amazon Redshift provides persistent storage for structured data. It uses columnar storage and compression techniques to optimize storage and query performance.

  4. Pricing: AWS Lambda pricing is based on the number of requests and the duration of function execution. Users only pay for the compute time consumed by their functions. On the other hand, Amazon Redshift pricing is based on the number of nodes and the amount of data stored. It offers various pricing options such as on-demand, reserved, and managed, depending on the user's needs.

  5. Querying: AWS Lambda does not provide a SQL interface or out-of-the-box querying capabilities. It is mainly used for executing functions in response to events. On the other hand, Amazon Redshift supports SQL-based querying and provides a comprehensive set of SQL functions and analytical capabilities for querying and analyzing data stored in the Redshift cluster.

  6. Data Sources: AWS Lambda can be triggered by various event sources such as API Gateway, S3, DynamoDB, or custom events. It can process data from multiple sources and integrate with other AWS services. On the other hand, Amazon Redshift can ingest data from various sources including AWS services, on-premises databases, third-party applications, and streaming data sources using AWS Data Pipeline, COPY command, or streaming services like Amazon Kinesis.

In summary, AWS Lambda is a serverless compute service, optimized for executing small, stateless functions in response to events, while Amazon Redshift is a fully-managed data warehousing solution, optimized for processing and analyzing large volumes of structured data.

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Advice on Amazon Redshift, AWS Lambda

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments
Mark
Mark

Nov 2, 2020

Needs adviceonMicrosoft AzureMicrosoft Azure

Need advice on what platform, systems and tools to use.

Evaluating whether to start a new digital business for which we will need to build a website that handles all traffic. Website only right now. May add smartphone apps later. No desktop app will ever be added. Website to serve various countries and languages. B2B and B2C type customers. Need to handle heavy traffic, be low cost, and scale well.

We are open to either build it on AWS or on Microsoft Azure.

Apologies if I'm leaving out some info. My first post. :) Thanks in advance!

133k views133k
Comments
datocrats-org
datocrats-org

Jul 29, 2020

Needs adviceonAmazon EC2Amazon EC2TableauTableauPowerBIPowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

  • keep raw and transformed data available to users to draft their own queries efficiently
  • give users the ability to give custom permissions and SSO
  • move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k
Comments

Detailed Comparison

Amazon Redshift
Amazon Redshift
AWS Lambda
AWS Lambda

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.;Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.;No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>
Extend other AWS services with custom logic;Build custom back-end services;Completely Automated Administration;Built-in Fault Tolerance;Automatic Scaling;Integrated Security Model;Bring Your Own Code;Pay Per Use;Flexible Resource Model
Statistics
Stacks
1.5K
Stacks
26.0K
Followers
1.4K
Followers
18.8K
Votes
108
Votes
432
Pros & Cons
Pros
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
Pros
  • 129
    No infrastructure
  • 83
    Cheap
  • 70
    Quick
  • 59
    Stateless
  • 47
    No deploy, no server, great sleep
Cons
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    Can't execute PHP w/o significant effort
Integrations
SQLite
SQLite
MySQL
MySQL
Oracle PL/SQL
Oracle PL/SQL
No integrations available

What are some alternatives to Amazon Redshift, AWS Lambda?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Azure Functions

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Serverless

Serverless

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

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