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How Netflix Orchestrates Millions of Workflow Jobs with Maestro
How Netflix Orchestrates Millions of Workflow Jobs with Maestro
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Disclaimer: The details in this post have been derived from the articles written by the Netflix engineering team. All credit for the technical details goes to the Netflix Engineering Team. The links to the original articles and videos are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Netflix originally used an orchestrator named Meson to manage its growing number of data and machine learning (ML) workflows.
For several years, Meson served the company well. It was responsible for scheduling and managing a large volume of tasks, approximately 70,000 workflows and 500,000 daily jobs. These jobs supported various business functions, including data transformation, recommendation models, A/B tests, and other ML-based decision systems.
However, as Netflix’s use of data grew, so did the pressure on Meson. The system began to show signs of strain, especially during periods of peak traffic, such as midnight UTC, when a large number of workflows were typically triggered. During these times, the orchestrator experienced slowdowns that led to increased operational overhead. Engineers on call had to closely monitor the system, especially during off-hours, to ensure it didn’t fail under the load.
A key limitation of Meson was its architecture. It was built using a single-leader model, which meant there was one main node responsible for coordinating all activity. Although it had high availability, the team had to vertically scale (i.e., upgrade to more powerful machines) to keep up with the growing demand. Eventually, they approached the limits of what AWS instance types could offer, making it clear that vertical scaling was not a sustainable long-term solution.
At the same time, the rate of growth was dramatic. The number of workflows in the system was doubling year over year, which added to the urgency of finding a better solution.
This need led to the creation of Maestro: a next-generation workflow orchestrator designed to overcome Meson’s bottlenecks.
Maestro was built with a distributed, scalable architecture from the ground up, capable of handling massive volumes of workflows and jobs while maintaining high reliability and low operational overhead. It was also designed to support a wide range of users and use cases, from engineers and data scientists to business analysts and content producers, making it a more versatile platform for the future of data and ML at Netflix.
In this article, we’ll look at how Netflix designed Maestro and the challenges they faced along the way.
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Maestro Architecture
At the heart of Maestro's design is a microservices-based architecture that allows the system to scale efficiently.
The system is broken down into three primary services, each responsible for a different part of the workflow orchestration. The diagram below shows this architecture in detail:
Workflow Engine
The Workflow Engine is the core component of Maestro. It manages the full lifecycle of workflows from the initial definition to step-by-step execution and completion.
In Maestro, a workflow is represented as a Directed Acyclic Graph (DAG) made up of individual units of work called steps (also referred to as jobs). These steps can include metadata, dependencies, runtime parameters, conditions, and branching logic.
A few key responsibilities of the Workflow Engine include:
Managing workflow definitions, including versioning and metadata.
Executing workflows by managing step instances, which are the runtime versions of each step.
Supporting advanced data patterns like foreach loops, conditional branches, and subworkflows.
Allowing steps to be reused across workflows through step templates.
Enabling dynamic workflows by supporting code-based parameter injection through a secure custom expression language called SEL (Simple Expression Language).
Providing a timeline view of workflow state changes to help with debugging and tracking.
Under the hood, the engine uses Netflix’s open-source Conductor library to manage the workflow state machine.
Time-Based Scheduling
Maestro supports workflows that need to be triggered at regular time intervals through the time-based scheduling service. Users can define when workflows should run using cron expressions or predefined time intervals such as hourly or weekly schedules.
This service is lightweight and designed to scale, offering an at-least-once triggering guarantee.
However, the Maestro engine includes logic to deduplicate triggers, which means that even if the scheduler tries to trigger a workflow multiple times, it ensures that the workflow is only executed once. This provides exact-once execution in practice.
Signal Service
Time-based scheduling is especially useful for recurring tasks, but it may not always be the most efficient method. For instance, running a workflow at midnight might not make sense if the data it depends on isn’t ready yet. To address that, Maestro includes another option for triggering workflows: the signal service.
The signal service provides support for event-driven orchestration, which complements time-based scheduling by triggering workflows or steps based on real-time events instead of a fixed time.
A signal is essentially a small message that carries information (such as metadata or parameter values) related to a triggering event. This service plays a crucial role in enabling conditional, responsive execution. Signals can come from:
Internal sources, such as other workflows.
External systems, such as S3 events, data warehouse table updates, or other infrastructure changes.
See the diagram below:
The Signal Service is used in two primary ways:
Triggering entire workflows when a signal matches a subscribed condition.
Gating individual steps within a workflow, where steps can wait for a signal to arrive before they begin execution.
In addition to these capabilities, the Signal Service also tracks signal lineage. This means it can trace which workflows were triggered by which signals, creating a dependency map between upstream and downstream workflows.
Scalability Techniques for Maestro
One of the core strengths of Maestro is its ability to scale horizontally and handle very large workloads without compromising reliability or performance.
There are a few ways Maestro achieves this:
At the architectural level, all of Maestro’s services are built as stateless microservices. This means that any instance of a service can handle any request, and there is no need to store session or workflow data within the service itself. Instead, all state information is stored in an external system. This design makes it easy to scale out services by simply adding more instances.
To handle communication between components and coordinate workflow execution, Maestro uses distributed queues. These queues decouple the services from each other, so each one can process requests at its own pace. They also allow for reliable message passing across the system, even when traffic spikes or failures occur in individual components.
For persistent storage of workflow definitions, step instances, and state data, Maestro uses CockroachDB. CockroachDB is a distributed SQL database that provides strong consistency guarantees. It’s well-suited for Maestro because it can scale horizontally and maintain high availability without much operational complexity. This ensures that all workflow and step data is stored reliably, even at a very large scale.
Another important aspect of Maestro’s scalability comes from how it handles workflows with massive numbers of steps. In many use cases, a single workflow might need to run the same job tens or even hundreds of thousands of times. For example, to backfill data over a long historical period or to train models across different parameters. Writing out all of these steps manually in a workflow definition would be impractical and hard to manage.
Moreover, Maestro supports nested foreach loops, meaning that one foreach block can contain another. This allows users to construct complex, large-scale workflows that execute millions or even billions of individual steps within a single workflow instance, all while remaining manageable and performant.
See the diagram below that shows the forEach design for Maestro.
Maestro Execution Abstractions
Maestro is designed to serve a wide range of users from beginner data analysts to advanced engineers. Therefore, it provides multiple execution abstractions to make it easier to define and run workflows.
Some abstractions are as follows:
Step Types
Maestro includes a set of predefined step types that encapsulate common tasks.
These step types represent standard operations, such as running a Spark job, moving data between systems (like from a database to a spreadsheet), or executing a SQL query.
When a user chooses a step type, they only need to supply the required parameters (such as the query string, memory allocation, or table name), and Maestro handles everything else behind the scenes.
For example, if a user selects the "Spark" step type, they can simply provide the Spark SQL query, along with resource requirements, and Maestro will schedule and execute the job accordingly. If the internal logic for Spark jobs ever needs to be updated (such as changing how jobs are submitted), the Maestro team can make that change once, and it will automatically apply to all workflows that use that step type.
Notebook Execution
Maestro also supports direct execution of Jupyter notebooks, which is useful for users who prefer working in a notebook environment or want to run code that has already been developed interactively.
These users can pass their notebooks to Maestro, and the system will schedule and run them as part of a workflow. This is especially helpful for data scientists or analysts who may not want to write workflow logic from scratch but still need to run periodic jobs.
Notebook execution integrates with other workflow steps, allowing users to include notebooks alongside other jobs within a larger DAG. Parameters can also be passed into notebooks dynamically, making them flexible components in a pipeline.
Docker Jobs
For more advanced users who need complete control over the execution environment, Maestro supports running Docker containers. These users can package their custom business logic into a Docker image, define the necessary input parameters, and let Maestro schedule and manage the container’s execution.
This option allows users to run any logic, regardless of programming language or dependencies, as long as it is packaged inside a container.
Maestro DSL Interface
To make it easy for users with different technical backgrounds to define workflows, Maestro supports multiple ways to create and manage them. Users can choose from a set of Domain Specific Languages (DSLs), graphical interfaces, or programmatic APIs, depending on what works best for their needs.
Maestro supports the following DSLs:
YAML: This is the most popular option because it is simple, human-readable, and easy to write. YAML is ideal for defining the structure of workflows, including steps, parameters, and dependencies, without needing to write actual code.
Python: For users who prefer scripting and want to dynamically generate workflows, Python DSL provides a more programmable approach. It’s useful when logic needs to be calculated or generated on the fly.
Java: Java DSL is available for engineers who work within Java-based systems or want to integrate workflows more closely with Java applications.
Source: Netflix Tech Blog Apart from these DSLs, users can also define workflows in the following ways:
API (JSON): Maestro exposes a set of APIs that allow users to submit workflow definitions directly in JSON format. This is helpful for automation or integration with other systems.
Maestro UI: A graphical user interface is provided for users who prefer not to write code. The UI allows users to visually create workflows by selecting and configuring steps through clicks and forms. This is especially useful for non-engineers or users who want a quick and easy way to build workflows without touching code.
Metaflow Integration: Maestro integrates with Metaflow, a library developed by Netflix that allows users to define DAGs in a Pythonic way. Users can write regular Python code using Metaflow and then run those workflows through Maestro. This bridges the gap between experimentation and production for data scientists.
Parameterized Workflow Example
A key feature of Maestro is its support for parameterized workflows, which allow workflows to adapt dynamically at runtime based on input values. This reduces duplication, simplifies maintenance, and enables powerful use cases.
One example is a backfill window, which is commonly used to process historical data across a range of dates.
Here’s how it works:
Step 1 – Compute the Date Range: The first step of the workflow uses a simple job (called a NoOp job) to calculate the list of dates that need to be backfilled. For example, it might generate all dates between 2021-01-01 and 2022-01-01. This list of dates is stored as an output parameter.
Step 2 – Foreach Loop Over Dates: The workflow then enters a foreach step that iterates over each date in the list produced by Step 1. For every date, it creates an iteration that runs the same set of jobs, each with a different date value as input.
Step 3 – Run the Backfill Job for Each Date: Inside the foreach, a job is defined to perform the actual backfill operation. In this example, it's a notebook job that runs a Jupyter notebook. The date from the current iteration is passed into the notebook as a parameter, so each run processes data for a specific day.
See the diagram below:
This setup allows users to create a single workflow definition that can dynamically expand into hundreds or thousands of job runs, each tailored to a different date, without having to manually write out every step.
Aggregated View and Rollup
When dealing with large-scale workflows, especially those that include subworkflows, foreach loops, or deeply nested execution paths, it can become difficult for users to keep track of what's happening during and after execution. To solve this, Maestro provides two helpful features: Aggregated View and Rollup.
Aggregated Views
The aggregated view is designed to give users a high-level summary of how a workflow has performed across multiple runs.
See the diagram below:
Instead of manually going through every instance of a workflow execution, users can see a combined overview of statuses across all runs. This includes information such as how many instances:
Succeeded
Failed
Were skipped
Are still running
This helps users quickly understand the overall behavior and health of a workflow over time, making it easier to spot patterns or recurring issues.
Rollup
The rollup feature goes a step further by providing a flattened summary of all the steps in a workflow, even when the workflow contains subworkflows, foreach iterations, or nested loops.
In large workflows, a single step can itself be a foreach loop or a call to another workflow, which then includes its steps. Rollup pulls all of this structure together into a unified view.
With rollup, users can:
See the total count of steps across all levels of the workflow.
Track how many of those steps succeeded, failed, were skipped, or were retried.
Get recursive visibility, meaning it looks deep into all execution layers to give a complete picture.
Event Publishing
Maestro is designed not just to run workflows but also to integrate smoothly with other systems at Netflix. One of the key ways it achieves this is through a robust event publishing system, which keeps various services informed about what is happening inside the orchestration platform in real time.
See the diagram below:
There are two main types of events that Maestro generates.
1 - Internal Events
These are events that describe the internal lifecycle of a workflow. They include things like:
When a workflow starts
When a step begins execution
When a step or workflow completes, fails, or is retried
When a workflow is paused, resumed, or cancelled
These events are used within Maestro itself and by other internal Netflix systems to track execution status, handle retries, and support user notifications or UI updates. They help ensure that the orchestration engine behaves correctly and reliably, especially under high workloads.
2 - External Events
Maestro can also publish events to external messaging systems such as Kafka or SNS (Simple Notification Service).
These events are typically consumed by other systems that need to react to changes in workflow state. For example:
A data warehouse system might start preparing data once it knows a data pipeline workflow has completed.
A monitoring system might trigger alerts if a critical workflow fails.
A downstream job might begin execution only after receiving confirmation that the upstream job has succeeded.
Some examples of events published externally include:
Workflow definition changes, such as when a workflow is updated, versioned, or deleted.
Workflow instance state transitions, like when an instance moves from “scheduled” to “running” or “failed” to “succeeded.”
By publishing these events, Maestro allows other services in the Netflix ecosystem to stay in sync with what's happening in the orchestration platform.
Conclusion
Maestro is Netflix’s answer to the growing complexity and scale of modern data and machine learning workflows.
Designed as a horizontally scalable, cloud-native orchestrator, it addresses the limitations of its predecessor, Meson, by providing a robust architecture built on stateless microservices, distributed queues, and a strong consistency layer via CockroachDB.
Its support for high-level execution abstractions, dynamic parameterization, and deep modularity through features like foreach, subworkflows, and reusable templates allows users to build scalable, maintainable pipelines.
With multiple DSLs, a visual UI, and integrations like Metaflow, Maestro is accessible to a broad range of users, from engineers to analysts. Its advanced monitoring features, such as signal lineage, rollups, and event publishing, ensure transparency and real-time observability across workflows.
As Netflix continues to grow its data infrastructure, Maestro lays a strong, flexible foundation for the future of intelligent workflow orchestration. It has also been made open-source.
References:
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A leader’s guide to keeping customers happy and loyal
Leading Off
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Welcome to the latest edition of Leading Off. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
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For businesses in all sectors, providing the best possible customer experience (CX) is a must. It’s also an increasingly complex challenge. CX strategies—which encompass everything a company does to deliver high-quality experiences, value, and growth to customers—are changing rapidly, as younger consumers exert more influence and technology reshapes the ways that both B2C and B2B organizations fulfill their customers’ needs. This week, we look at how leaders in various industries can help their organizations create an exceptional CX.
Consumers increasingly expect more convenience, cost transparency, and personalized engagement from businesses of all kinds. Healthcare offers one example. Many organizations in the healthcare industry have been slow to adapt to customers’ preferences, giving both providers and payers a substantial opportunity to deliver a better CX and improve business results by addressing consumers’ evolving expectations. According to McKinsey Partner Jenny Cordina and her coauthors, three McKinsey surveys of US consumers revealed low to medium satisfaction with many elements of their healthcare journeys. The authors recommend six steps for upgrading the healthcare CX, including more expedient options for care, more competitive pricing, and the use of digital tools in health management. “Leaders of healthcare organizations who invest in building strong consumer foundations will be well positioned to compete in a market that’s increasingly empowering consumers to own their healthcare journeys,” the authors say.
That’s how much faster revenue grew for companies with higher CX ratings compared with “CX laggards” between 2016 and 2021, according to McKinsey research on how organizations can better serve their existing customers. McKinsey’s Harald Fanderl, Oliver Ehrlich, Robert Schiff, and Victoria Bough also note that revenues of CX leaders rebounded more quickly from the COVID-19 pandemic than those of other organizations.
That’s McKinsey Senior Partners Alexander Sukharevsky and Emily Reasor and their coauthors on how retail companies can generate value from gen AI. One important tool is the AI-powered chatbot, which can recommend products, help customers learn more about certain items or companies, or manage a customer’s virtual shopping cart. “Since many consumers will use these chatbots before deciding to purchase a product rather than after, using chatbots allows retailers to engage with customers earlier in their shopping journey, which can help increase customers’ overall satisfaction,” the authors say.
Shortages of certain products during the COVID-19 pandemic gave rise to a phenomenon of weakened brand loyalty that continues to shape consumer activity. “A lot of these behaviors stuck: switching brands, switching channels, exploring alternatives,” Senior Partner Sajal Kohli says in an episode of The McKinsey Podcast on the state of consumer behavior. A McKinsey survey of 15,000 consumers revealed that more than one-third of shoppers in more developed markets had tried different brands over the previous three months, and some 40 percent had switched retailers to find better prices or discounts. “That is pretty unprecedented in terms of consumer choices,” Kohli notes. These changes suggest that companies will need to dig deeper to understand consumers’ desires and motivations. “It’s about investing in data and advanced capabilities to really understand consumers at an individual, small, or microsegment level,” Kohli says.
Advertising and marketing campaigns that deliver “surprise and delight”—from scrappy viral marketing campaigns to big-money Super Bowl commercials—are time-tested tools for wooing new customers. But the importance of delighting customers goes far beyond getting them to buy for the first time. Delight is critical to customer loyalty and business growth, according to McKinsey’s Ankit Bisht and Sangeeth Ram and their coauthors. Through surveys of 25,000 customers across industries, including tourism, insurance, and banking, they found that companies that prioritize customer delight outperform their competitors in both revenue growth and total shareholder returns. Their research also indicates that service excellence and product innovation are the biggest factors in keeping customers happy and coming back to the company. “By prioritizing customer delight and implementing smart, sustainable strategies, companies can unlock new frontiers of service and drive lasting returns without incurring prohibitive expenses,” the authors say.
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How are executives’ views on the economy changing?
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Mounting uncertainty. In the midst of numerous policy changes, uncertainty looms over views on the economy. In a recent McKinsey Global Survey from March, Senior Partner and McKinsey Global Institute Chair Sven Smit and coauthors found that respondents now view shifts in trade policy and geopolitical instability—which was long seen as the top risk to growth—as equally disruptive forces. In addition, respondents are much more likely now than they were in December to predict a decline in economic conditions, both worldwide and in their own countries.
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Product Catalog Request
Dear Sir/Madam,
I am writing to express the interest of MAGSI INTERNATIONAL in exploring purchasing opportunities with your esteemed organization. My name is Dominique Floch, and I would be grateful if a sales manager could respond to this email at their earliest convenience.
In order to facilitate our evaluation, could you please provide us with an updated price list of your products for our review? We would appreciate it if this information could be shared with us as soon as possible.
Thank you for your prompt attention to this matter, and we look forward to hearing from you soon.
Best regards,
Dominique Floch
MAGSI INTERNATIONALDominique FLOC'H
Responsable Export, Grands Comptes et Loueurs
+33(0) 609 14 54 28
+33(0) 298 24 12 85
dominiquefloch805@gmail.com 
www.magsi.fr 
MAGSI, ZA de Bel Air, 29 450 SIZUN
by "Dominique Floc'h" <dominiquefloch805@gmail.com> - 07:32 - 13 Apr 2025 -
Exploring Partnership Opportunities in CNC Machining
Dear info
OI Teach specialist in the cnc machining manufacture for 10 years. And we have 200 CNC machining machines.We have been made the aluminum part for the medical device ,car parts,motocycle parts and bicycle accessories .We have a lot of customers from all over the world.like Ge/LG.We can do 5 axis /4axis/3 axis /leather etc.The material we can do is AL6061 /AL7075 /AL6063 /AL5052 /Stainless Steel /Brass /Copper /ect..The finishing we can do is the oxidation with all color /chroming/hard oxidation/powder coating.
Please send your file step/stp/igs format to me to quote.
OI Tech expecting establish long business relationship with you .
Best Regards
by "Shital Sabi" <shitalsabi96@gmail.com> - 03:33 - 13 Apr 2025 -
Laminator, improve your packaging efficiency
Dear info,
This is Jessie from LP Laminator factory from Hangzhou,China. We are a leading manufacturer of cold laminator, low-heat laminator, thermal laminator, no bottom film laminator and ect. we have been focus on laminator for more than 12 years. We have competitive price .
We are looking forward to starting kindly and long-term business with you. Wish to get your back.
Sincerely,
Jessie Li
Hangzhou Zhanqian Technology Co., Ltd
Tel: 0086-18905869797
Skype:lxf0925@163.com
Email:lxf0925@163.com
Website:https://chinalingpai.en.alibaba.com
by "Jarlin Mancha" <manchajarlin@gmail.com> - 02:36 - 13 Apr 2025 -
Interactive Podium Monitor
About us Product Contact 
Features of QIT 600F3
Precision Pen
Touch
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Dual - Protection Screen
Real-Time Sync for Operations and Annotations
READ MORE 
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Chat support 24/7
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QOMOEDU.COM Click the link above to visit our website.
About us Product Solution Contact us Email:galen@qomo.com Tel:+86 183 5911 0265 
Simple, intuitive solutions that empower everyone to focus on what they do best.
by "Uyetake Wahlund" <uyetakewahlund@gmail.com> - 11:41 - 12 Apr 2025 -
Quotation and Specifications for Milk Thistle Extract from Xi’an Demeter Biotech Co., Ltd
Dear info,
Glad to know you are interested in Milk thistle extract by google. We, Xi’an Demeter Biotech Co., Ltd, specialize in offering animal and plant active ingredients to Chinese traditional medicine, nutrition, cosmetics and addictive since 2008. Milk thistle extract is our key product.
As for Milk thistle extract,
(1) Price sheetItem
Descriprion
Qty.
Unit
Unit Price
FOB China
1
Milk Thistle Extract Powder 80% Sylimarin
25
kg
US$33.50
US$837.50
2
Milk Thistle Extract Granular 80% Sylimarin
25
kg
US$35.85
US$896.25
Here are popular specs for application.
Application
Effect
Part of Specs
Medicine
Protect the liver
Silymarin 80% UV, Silybin 80% -99%
Health products
Protect the liver
Silymarin 80% UV, Silybin 80% -100%
Food
Protect the liver
Silymarin 80% UV, Silybin 80% -101%
Cosmetics
anti-radiation, anti-aging, anti-wrinkle
Silymarin 80% UV
Sports health drink
Protect the liver
Silymarin 40% UV
Feed grade
Protect the liver
Silymarin 80% UV / 30% HPLC, 75% UV
If other specification and quantity is wanted, tell me. Or your usage is offered, better specification will be recommended.
Your prompt reply will be very much appreciated.Best regards
by "Emma Swift" <atcrussur@gmail.com> - 09:44 - 12 Apr 2025 -
Nanocrystalline cores for EMC common mode choke
Dear Sir/Madam,
We're professional manufacturer of high performance amorphous and nanocrystalline cores. Nanocrystalline core's inductance is about 5 times higher than ferrite cores, it is very easy to achieve high inductance, suitable for common mode choke.
Over 10 yeas experience in manufacturing nanocrystalline cores, our cores have similar performance compared to cores from VAC, Hitachi, Magnetec.
In case you have any interest or inquiry, please feel free to let us know.
Best Regards,
KM Core Team
King Magnetics
www.kingmagnetics.com
core@kingmagnetics.com
by "km12" <km12@kingmagcores.com> - 04:02 - 12 Apr 2025 -
The week in charts
The Week in Charts
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:14 - 12 Apr 2025 -
EP158: How to Learn API Development
EP158: How to Learn API Development
Forwarded this email? Subscribe here for moreIntroducing Augment Agent: an AI Agent for pro software engineers (Sponsored)
Augment Code is the first AI coding Assistant built for professional software engineers, large codebases, and production-grade projects.
Now they're excited to unveil Augment Agent, a powerful new mode for Augment Code that can help you complete software development tasks end-to-end. Agent can:
Add new features spanning multiple files
Queue up tests in the terminal
Open Linear tickets, start a pull request
Start a new branch in GitHub from recent commits
...and all kinds of other things. Skip the toy AI coding projects and build something real -- Augment Agent is
standing by to help.This week’s system design refresher:
How to Learn API Development?
Must-Know Network Protocol Dependencies
Top AI Coding Tools for Developers You Can Use in 2025
18 Key Design Patterns Every Developer Should Know
Hiring 🚀: ByteByteGo’s First Sales & Partnerships Hire
SPONSOR US
How to Learn API Development?
Learning how to develop APIs is an important skill for modern-day developers. Here’s a mind map of what all you need to learn about API development:
API Fundamentals
What is an API, types of API (REST, SOAP, GraphQL, gRPC, etc.), and API vs SDK.API Request/Response
HTTP Methods, Response Codes, and Headers.Authentication and Security
Authentication mechanisms (JWT, OAuth 2, API Keys, Basic Auth) and security strategies.API Design and Development
RESTful API principles include stateless, resource-based URL, versioning, and pagination. Also, API documentation tools like OpenAPI, Postman, Swagger.API Testing
Tools for testing APIs such as Postman, cURL, SoapUI, and so on.API Deployment and Integration
Consuming APIs in different languages like JS, Python, and Java. Also, working with 3rd party APIs like the Google Maps API and the Stripe API. Learn about API Gateways like AWS, Kong, Apigee.
Over to you: What else will you add to the list for learning API development?
Must-Know Network Protocol Dependencies
Understanding network protocol dependencies is essential for cybersecurity and networking. Here’s a quick understanding of the same:
IPv4 and IPv6 are the foundation of all networking. ICMP and ICMPv6 handle diagnostics, while IPsec ensures secure communication.
TCP and UDP support various protocols. SCTP and DCCP serve specific cases.
Some TCP-based protocols are HTTP, SSH, BGP, RDP, IMAP, SMTP, POP, etc.
UDP-based protocols are DNS, DHCP, SIP, RTP, NTP, etc.
SSL/TLS encrypts HTTPS, IMAPS, and SMTPS.
LDAP and LDAPs are used for directory services over TCP and secured with SSL/TLS.
QUIC is a UDP-based replacement for TCP+TLS for faster, encrypted connections.
MCP or Model Context Protocol is an emerging standard for communicating with LLMs.
Over to you: Which other network protocol will you add to the list?
Top AI Coding Tools for Developers You Can Use in 2025
AI Code Assistants
GitHub Copilot: Code completion and automatic programming tool.
ChatGPT: Helps write and debug code with the latest models.
Claude: Recent and specialized coding knowledge to generate accurate and up-to-date code.
Amazon CodeWhisperer: AI Assistant in the IDEAI-Powered IDEs
Cursor: AI-powered IDE for Windows, macOS, and Linux.
Windsurf: AI-powered IDE that tackles complex tasks independently.
Replit: Create fully working apps to go live fast.Team Productivity
Cody: The enterprise AI code assistant for writing, fixing, and maintaining code.
Pieces: AI-enabled productivity tool to help developers manage code snippets.
Visual Copilot: Convert Figma designs into React, Vue, Svelte, Angular, or HTML code.Code Quality and Completion
Snyk: Real-time vulnerability scanning of human and AI-generated code.
Tabnine: A code completion tool to accelerate software development.
Over to you: Which other AI Coding Tool will you add to the list? What’s your favorite?
18 Key Design Patterns Every Developer Should Know
Patterns are reusable solutions to common design problems, resulting in a smoother, more efficient development process. They serve as blueprints for building better software structures. These are some of the most popular patterns:
Abstract Factory: Family Creator - Makes groups of related items.
Builder: Lego Master - Builds objects step by step, keeping creation and appearance
Prototype: Clone Maker - Creates copies of fully prepared examples.
Singleton: One and Only - A special class with just one instance.
Adapter: Universal Plug - Connects things with different interfaces.
Bridge: Function Connector - Links how an object works to what it does.
Composite: Tree Builder - Forms tree-like structures of simple and complex parts.
Decorator: Customizer - Adds features to objects without changing their core.
Facade: One-Stop-Shop - Represents a whole system with a single, simplified interface.
Flyweight: Space Saver - Shares small, reusable items efficiently.
Proxy: Stand-In Actor - Represents another object, controlling access or actions.
Chain of Responsibility: Request Relay - Passes a request through a chain of objects until handled.
Command: Task Wrapper - Turns a request into an object, ready for action.
Iterator: Collection Explorer - Accesses elements in a collection one by one.
Mediator: Communication Hub - Simplifies interactions between different classes.
Memento: Time Capsule - Captures and restores an object's state.
Observer: News Broadcaster - Notifies classes about changes in other objects.
Visitor: Skillful Guest - Adds new operations to a class without altering it.
Hiring 🚀: ByteByteGo’s First Sales & Partnerships Hire
Location: Remote
Compensation: Based on experience and hours committed
Experience Level: 1+ years in digital media sales or partnerships
About ByteByteGo
ByteByteGo is the largest technology newsletter on Substack, breaking down complex software and system design concepts into clear, visual, and engaging formats. Our weekly newsletter reaches over a million developers, engineers, and tech leaders across the world.
About the Role
We’re looking for a sales and partnerships lead who will help grow our newsletter sponsorship business. This role will focus on securing new advertisers, nurturing existing relationships, and optimizing revenue opportunities across our newsletter and other media formats.
What You’ll Do
- Identify, pitch, and close sponsorship deals with companies targeting a technical audience.
- Manage inbound interest and proactively source new leads
- Handle sales calls and follow-ups with prospective sponsors
- Grow existing accounts through renewals, upsells, and relationship management
- Track performance and help optimize pricing, packages, and positioning
What We’re Looking For
- 1+ years of experience in sales, partnerships, or media sponsorships—ideally in tech or newsletter media
- Excellent written and verbal communication skills
- Experience with outbound prospecting, CRM tools, and closing deals
- Strong understanding of the tech ecosystem—developer tools, SaaS, AI, startups
- Bonus: you’re already a fan of ByteByteGo and get our voice and audience
- You’re self-motivated, organized, and ready to grow with a small team
How to Apply
Send your resume and a short note on why you’re excited about this role to jobs@bytebytego.comSPONSOR US
Get your product in front of more than 1,000,000 tech professionals.
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by "ByteByteGo" <bytebytego@substack.com> - 11:37 - 12 Apr 2025 -
Last chance to join the “new & improved” OFA!
Russell kicks off the new One Funnel Away Challenge MONDAY morning!! If you haven’t joined yet… This is your last chance.
I’d recommend you sign up now so you have a little time to orient yourself and prepare to hit the ground running on Day 1.
You’ll be drinking from a fire hose… So, get registered and prepped now!
Expert Or Ecom - Which OFA Track Is Right For You? (Starts Next Monday) >>
They’ve got 30 days of step-by-step coaching lined up…
And whether you pick TEAM RUSSELL… or TEAM TREY…
You can’t go wrong!! This new and improved OFA is going to change your life. I can feel it!
Register now and don’t wait!!!
We’re starting first thing on Monday morning!
Expert Or Ecom - Which OFA Track Is Right For You? (Starts Next Monday) >>
See you in OFA!Todd Dickerson
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by "Todd Dickerson" <noreply@clickfunnelsnotifications.com> - 11:32 - 12 Apr 2025
