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Virtual Summit: Integration Insights Live
With every new app, your IT landscape becomes more complex—but managing it doesn’t have to be.
Join us for an exclusive series of virtual events dedicated to the transformative power of integration. Explore how an integration platform as-a-service (iPaaS) can drive the modernization of your IT infrastructure and create a solid foundation for future innovation. Across five expert-led sessions, explore the latest research, trends, and strategies, to learn about:
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- Unlocking the potential of AI with integration
Integration Insights Live: Innovate with a Modern iPaaS
April 3, 2025
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by "SAP Integration Suite team" <sap@mailsap.com> - 07:12 - 20 Mar 2025 -
Monolith vs Microservices vs Modular Monoliths: What's the Right Choice
Monolith vs Microservices vs Modular Monoliths: What's the Right Choice
Forwarded this email? Subscribe here for moreLatest articles
If you’re not a subscriber, here’s what you missed this month.
Dark Side of Distributed Systems: Latency and Partition Tolerance
Non-Functional Requirements: The Backbone of Great Software - Part 2
Non-Functional Requirements: The Backbone of Great Software - Part 1
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In the ever-evolving landscape of software development, the choice of architecture can make or break a project.
It’s not merely a technical decision but a strategic one that shapes how efficiently a team can build, scale, and maintain an application. Whether crafting a simple tool or a complex system, the architecture selected lays the groundwork for everything that follows.
The architecture chosen influences not only how software is built but also how it grows and runs over time. A misstep can result in sluggish performance, tangled codebases, and frustrated teams. On the other hand, the right choice can streamline development, support seamless scaling, and make maintenance easier.
In this article, we explore the three key architectural styles: Monolith, Microservices, and Modular Monoliths. Each offers distinct approaches to structuring software, with unique benefits and challenges.
We’ll also examine the strengths and weaknesses of each approach, from the ease of a monolith to the resilience of microservices and the balanced design of a modular monolith. We’ll look at scenarios where switching from one architecture to another makes sense, equipping us with practical insights for navigating these critical decisions.
Monolithic Architecture...
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by "ByteByteGo" <bytebytego@substack.com> - 10:37 - 20 Mar 2025 -
API governance doesn’t have to be hard. Let’s show you how
API governance doesn’t have to be hard. Let’s show you how
Hi Md Abul,
Did you hear? We’re hosting a webinar that’s all about transforming the way you manage APIs. If you’ve been thinking about how to bring some control to your API ecosystem (without losing your sanity), then this one’s for you.
From chaos to confidence: Accelerating API governance maturity with Tyk
In this session, we’ll show you how a universal API governance platform can help your enterprise stay ahead of the game in today’s evolving API landscape. And let’s face it - staying ahead is the only option, right?
Why a universal API governance platform is key to keeping things consistent, secure, and future-ready.
How the API Governance Toolkit helps you level up your governance across teams and environments.
Strategies for enforcing governance at scale (no more chaos, promise).
How Tyk’s platform can make all of this easier, faster, and more reliable.
Plus, Budha Bhattacharya (our Developer Advocate) and Mark Boyd (API governance expert) will share practical insights about the realities of universal governance - what works, what doesn’t, and how you can put it all into action. Then, Carol Cheung (Tyk’s Senior Product Manager) will take you behind the scenes with a sneak peek at Tyk’s Universal API Governance Platform.
Ready to discover how to make governance easier and more scalable? We thought so.
📅 Date: March 27th 2025🕙 Time: 10 am EST/ 2 pm GMT
📍 Location: Zoom webinar, discussion, demo
See you in a week!,
Budha and team
Tyk, Huckletree 199 Bishopsgate, Broadgate, London, City of London EC2M 3TY, United Kingdom, +44 (0)20 3409 1911
by "Budha from Tyk" <budha@tyk.io> - 06:00 - 20 Mar 2025 -
(Video #3 Funnel Install) Jeff Walker’s Product Launch Funnel!
What’s up! Have you been loving the emails coming this week!?
On Day 1 Trey Lewellen broke down his Ecom funnel.
On Day 2 Dan Henry revealed his Webinar funnel.
Now, it’s time to hear from Jeff Walker!
GO HERE TO WATCH THE VIDEO FROM JEFF WALKER AND STEAL HIS PRODUCT LAUNCH FUNNEL FOR FREE! >>
I’m pretty sure Jeff literally invented the concept of a product launch funnel!!
But here’s the crazy part… You’ll see when you watch the video, but Jeff wasn’t some natural-born marketer. When he started, he was a stay-at-home dad with NO marketing experience, NO product, and NO clue how to sell…
But that didn’t stop him (super inspiring for a lot of people in his situation!)
His first launch made $1,650…
Then $6,000…
Then $34,000…
And eventually…
He had a million-dollar launch in a single hour!!!
And now in today’s video, he’s handing YOU the keys to the exact Product Launch Funnel template that’s been responsible for over a billion dollars in sales!!
GO HERE TO WATCH THE VIDEO FROM JEFF WALKER AND STEAL HIS PRODUCT LAUNCH FUNNEL FOR FREE! >>
This is just Day 3! We have 4 more to go! 🤯
Tomorrow, another top expert will break down their most successful funnel… so don’t miss it!
See you then!
Russell Brunson
P.S. - Don’t forget, you’re just one funnel away….
P.P.S - To dive in and start implementing what these experts are teaching, you’ll need to get a ClickFunnels account. If you don’t have one yet, I have a special 2-week FREE TRIAL link HERE >>
© Etison LLC
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If you purchase anything through a link in this email, you should assume that we have an affiliate relationship with the company providing the product or service that you purchase, and that we will be paid in some way. We recommend that you do your own independent research before purchasing anything.
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by "Russell @ ClickFunnels" <noreply@clickfunnelsnotifications.com> - 05:33 - 20 Mar 2025 -
Gen AI: How organizations are capturing value
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 12:06 - 20 Mar 2025 -
Tailored Seating Solutions for Your Projects
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by "Fujana shanr" <4750119@gmail.com> - 04:18 - 19 Mar 2025 -
Employees are already using AI. How can leaders catch up?
Five Fifty
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Many workers are already using AI, and business leaders who act on this momentum face a significant opportunity. Identifying four archetypes of employee sentiment can help companies understand where encouragement might be needed, say McKinsey’s Hannah Mayer, Lareina Yee, Michael Chui, and Roger Roberts. To learn more, check out the latest edition of the Five Fifty.
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by "McKinsey Quarterly Five Fifty" <publishing@email.mckinsey.com> - 04:03 - 19 Mar 2025 -
Your next coworker might be a gen AI agent
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
The impact of gen AI. Gen AI is relatively new, and users are still learning how to deploy it effectively. So says Kiran Prasad, CEO and cofounder of Big Basin Labs, in a recent episode of the At the Edge podcast with guest host McKinsey’s Brian Gregg. At the same time, AI-related innovation is accelerating. “With semiconductors, per Moore’s law, processor speeds double every two years. I expect the impact of gen AI is going to be four times every two years,” says Navin Chaddha, managing partner at Mayfield Fund, in the episode.
—Edited by Seth Stevenson, senior editor, New York
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 12:43 - 19 Mar 2025 -
(Video #2 Funnel Install) Dan Henry’s Webinar Funnel!
Hey!
Was yesterday’s presentation from Trey incredible, or what!??
For my Ecom people… This is an absolute must see presentation…
If you missed Trey Lewellen’s e-commerce funnel breakdown, go watch it here >>
Are you on pins and needles wondering who we’ll hear from today?? Drum roll please….
Dan Henry!!
GO HERE TO WATCH THE VIDEO FROM DAN HENRY AND STEAL HIS WEBINAR FUNNEL FOR FREE! >>
OK, so most of you know, Dan Henry, right?
If not… Dan… he was dead broke when he stumbled across my book, DotComSecrets.
Fast forward, he’s now done over $30 million in sales (all thanks to one simple strategy).
His secret? High-ticket webinars.
And today, he’s revealing exactly how he sells high-ticket offers on autopilot… and giving YOU the same webinar funnel template that’s made him millions.
GO HERE TO WATCH THE VIDEO FROM DAN HENRY AND STEAL HIS WEBINAR FUNNEL FOR FREE! >>
This is just Day 2! We have 5 more to go! 🤯
Tomorrow, another top expert will break down their most successful funnel… so don’t miss it!
See you then!
Russell Brunson
P.S. - Don’t forget, you’re just one funnel away….
P.P.S - To dive in and start implementing what these experts are teaching, you’ll need to get a ClickFunnels account. If you don’t have one yet, I have a special 2-week FREE TRIAL link HERE >>
© Etison LLC
By reading this, you agree to all of the following: You understand this to be an expression of opinions and not professional advice. You are solely responsible for the use of any content and hold Etison LLC and all members and affiliates harmless in any event or claim.
If you purchase anything through a link in this email, you should assume that we have an affiliate relationship with the company providing the product or service that you purchase, and that we will be paid in some way. We recommend that you do your own independent research before purchasing anything.
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by "Russell @ ClickFunnels" <noreply@clickfunnelsnotifications.com> - 09:46 - 18 Mar 2025 -
(Video #2 Funnel Install) Dan Henry’s Webinar Funnel!
Hey!
Was yesterday’s presentation from Trey incredible, or what!??
For my Ecom people… This is an absolute must see presentation…
If you missed Trey Lewellen’s e-commerce funnel breakdown, go watch it here >>
Are you on pins and needles wondering who we’ll hear from today?? Drum roll please….
Dan Henry!!
GO HERE TO WATCH THE VIDEO FROM DAN HENRY AND STEAL HIS WEBINAR FUNNEL FOR FREE! >>
OK, so most of you know, Dan Henry, right?
If not… Dan… he was dead broke when he stumbled across my book, DotComSecrets.
Fast forward, he’s now done over $30 million in sales (all thanks to one simple strategy).
His secret? High-ticket webinars.
And today, he’s revealing exactly how he sells high-ticket offers on autopilot… and giving YOU the same webinar funnel template that’s made him millions.
GO HERE TO WATCH THE VIDEO FROM DAN HENRY AND STEAL HIS WEBINAR FUNNEL FOR FREE! >>
This is just Day 2! We have 5 more to go! 🤯
Tomorrow, another top expert will break down their most successful funnel… so don’t miss it!
See you then!
Russell Brunson
P.S. - Don’t forget, you’re just one funnel away….
P.P.S - To dive in and start implementing what these experts are teaching, you’ll need to get a ClickFunnels account. If you don’t have one yet, I have a special 2-week FREE TRIAL link HERE >>
© Etison LLC
By reading this, you agree to all of the following: You understand this to be an expression of opinions and not professional advice. You are solely responsible for the use of any content and hold Etison LLC and all members and affiliates harmless in any event or claim.
If you purchase anything through a link in this email, you should assume that we have an affiliate relationship with the company providing the product or service that you purchase, and that we will be paid in some way. We recommend that you do your own independent research before purchasing anything.
Copyright © 2018+ Etison LLC. All Rights Reserved.
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by "Russell @ ClickFunnels" <noreply@clickfunnelsnotifications.com> - 09:46 - 18 Mar 2025 -
Exploring Collaboration Opportunities in Semiconductor Testing
Dear info,
Good day to you. I am reaching out on behalf of SEMISHARE, a semiconductor testing company established in 2010. With a strong focus on advanced probe stations and wafer test solutions, we have successfully completed over 1,000 projects, showcasing our expertise in various testing fields, including WAT/CP, I-V/C-V, RF/mmW, MEMS and optoelectronic testing etc.
Our commitment to quality and reliability has made us a trusted partner for chip designers and semiconductor manufacturers. We offer a range of products, from fully-automatic probers to semi-automatic and manual probers, tailored to meet diverse testing needs.
We pride ourselves on our 24/7 support, ensuring minimal delays in the fast-paced semiconductor industry. I would welcome the opportunity to discuss how we can support your testing requirements.
Thank you for considering SEMISHARE. I look forward to your response.
Best regards,
Tyeshia Winokur
by "Tyeshia Winokur" <winokurtyeshia@gmail.com> - 07:39 - 18 Mar 2025 -
Architecture project
Dear info,
Greeting from TEAM-E, wish all is going well with you.Established in 2004, we are still passionate, energetic & creative, and striving to be forefront of the CG industry. TEAM-E built up the most comprehensive service for digital visualization，specializing in：
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Welcome to view our Project Portfolio & contact for inquiryl. Wish partnering with you in the near future!
Best Regards,
Sarah Chow | Vice Manager
sarah@teame3d.com
by "cong" <cong@teame3d.com> - 06:54 - 18 Mar 2025 -
Navigating the Impact of Recent U.S. Tariffs on Digital Transformation Initiatives
Navigating the Impact of Recent U.S. Tariffs on Digital Transformation Initiatives
Hi MD Abul,
I wanted to address the recent developments in U.S. trade policy and their potential implications for our digital transformation efforts.
Recent U.S. Tariff Developments
On March 12, 2025, the U.S. government imposed 25% tariffs on all steel and aluminum imports, aiming to bolster domestic production. This move has prompted immediate retaliatory measures from key trading partners, including the European Union and Canada, escalating global trade tensions.
The Organization for Economic Cooperation and Development (OECD) has expressed concerns that these tariffs could slow global economic growth and increase inflation. Their projections indicate a downward revision of growth forecasts for major economies, particularly those heavily engaged in trade with the U.S.
Implications for Digital Transformation
These trade policy shifts can have significant ramifications for organizations embarking on digital transformation:
-
Supply Chain Disruptions: Increased tariffs may lead to higher costs for hardware and infrastructure components essential for digital initiatives.
-
Budget Reallocations: Organizations might need to adjust budgets to accommodate rising costs, potentially affecting funding for technology projects.
-
Strategic Reassessment: Companies may need to revisit their digital strategies to mitigate risks associated with an unpredictable trade environment.
Third Stage Consulting's Insights
In our 2025 Digital Transformation Report, we emphasize the importance of adaptability in the face of external economic pressures. Key recommendations include:
-
Diversifying Suppliers: Reducing reliance on a single region or supplier can mitigate risks associated with trade disputes.
-
Investing in Resilient Technologies: Adopting scalable and flexible technologies can help organizations adjust to changing economic conditions.
-
Continuous Monitoring: Staying informed about policy changes enables proactive adjustments to digital strategies.
For a more in-depth analysis, I encourage you to explore our full report here.
Please feel free to reach out if you have any questions or need assistance in navigating these developments.
Best regards,
Eric Kimberling
by "Eric Kimberling" <eric.kimberling@thirdstage-consulting.com> - 04:58 - 18 Mar 2025 -
-
Enhance Efficiency with Our Induction Cookers
Dear info,
Hope this email finds you in good health. I am Cindy from Guangdong Yipai Catering Equipment Co., Ltd., a leading provider of commercial induction cookers and electric ceramic stoves.
Our induction cookers offer:
1. Efficient and Energy-Saving: Up to 30% energy savings over traditional methods.
2. Intelligent Temperature Control: Automatically adjusts temperature for optimal cooking.I would be thrilled to discuss how our products can improve your kitchen operations. Looking forward to your feedback.
Best regards,
Cindy
Guangdong Yipai Catering Equipment Co., Ltd.
by "Marjorie Esther" <diann356478@gmail.com> - 04:44 - 18 Mar 2025 -
(SSW - Video #2) The Epiphany Bridge
What’s up!!?
Hope you LOVED Day 1 of the Story Selling Workshop!
Yesterday, we laid the foundation for how stories break down resistance, build trust, and make selling feel completely natural.
Today I’m sharing a little “secret” that literally makes people sell themselves!
It’s called the Epiphany Bridge.
Hear me out… People don’t like being “sold to.” They resist logic, facts, and data when making a buying decision. But they DO love hearing stories that spark an epiphany!!!
In today’s training, I’ll break down exactly how to craft your own Epiphany Bridge story and use it inside your sales funnels, emails, videos, and more.
So cool, right!?!
GO WATCH VIDEO 2 IN THE FREE STORY SELLING WORKSHOP HERE >>
We’re only half way done! I still have two more FREE training videos for you in this workshop. Watch for video #3 tomorrow!
See you then!
Russell Brunson
P.S. Don’t forget, you’re just one funnel away…
P.P.S. If you missed video #1 of Story selling, go here >>
Then… watch video #2 (“Epiphany Bridge”) here >>Marketing Secrets
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by "Russell Brunson" <newsletter@marketingsecrets.com> - 02:22 - 18 Mar 2025 -
How Netflix Stores 140 Million Hours of Viewing Data Per Day
How Netflix Stores 140 Million Hours of Viewing Data Per Day
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Disclaimer: The details in this post have been derived from the Netflix Tech Blog. All credit for the technical details goes to the Netflix engineering team. Some details related to Apache Cassandra® have been taken from Apache Cassandra® official documentation. Apache Cassandra® is a registered trademark of The Apache Software Foundation. The links to the original articles 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.
Every single day millions of people stream their favorite movies and TV shows on Netflix.
With each stream, a massive amount of data is generated: what a user watches, when they pause, rewind, or stop, and what they return to later. This information is essential for providing users with features like resume watching, personalized recommendations, and content suggestions.
However, Netflix's growth has led to an explosion of time series data (data recorded over time, like a user’s viewing history). The company relies heavily on this data to enhance the user experience, but handling such a vast and ever-increasing amount of information also presents a technical challenge in the following ways:
Every time a user watches a show or movie, new records are added to their viewing history. This history keeps growing, making it harder to store and retrieve efficiently.
As millions of people use Netflix, the number of viewing records increases not just for individual users but across the entire platform. This rapid expansion means Netflix must continuously scale its data storage systems.
When Netflix introduces new content and features, people spend more time watching. With the rise of binge-watching and higher-quality streaming (like 4K videos), the amount of viewing data per user is also increasing.
A failure to manage this data properly could lead to catastrophic user experience delays when loading watch history. It could also result in useless recommendations, or even lost progress in shows.
In this article, we’ll learn how Netflix tackled these problems and improved their storage system to handle millions of hours of viewing data every day.
The Initial Approach
When Netflix first started handling large amounts of viewing data, they chose Apache Cassandra® for the following reasons:
Apache Cassandra® allows for a flexible structure, where each row can store a growing number of viewing records without performance issues.
Netflix’s system processes significantly more writes (data being stored) than reads (data being retrieved). The ratio is approximately 9:1, meaning for every 9 new records added, only 1 is read. Apache Cassandra® excels in handling such workloads.
Instead of enforcing strict consistency, Netflix prioritizes availability and speed, ensuring users always have access to their watch history, even if data updates take a little longer to sync. Apache Cassandra® supports this tradeoff with eventual consistency, meaning that all copies of data will eventually match up across the system.
See the diagram below, which shows the data model of Apache Cassandra® using column families.
To structure the data efficiently, Netflix designed a simple yet scalable storage model in Cassandra®.
Each user’s viewing history was stored under their unique ID (CustomerId). Every viewing record (such as a movie or TV show watched) was stored in a separate column under that user’s ID. To handle millions of users, Netflix used "horizontal partitioning," meaning data was spread across multiple servers based on CustomerId. This ensured that no single server was overloaded.
Reads and Writes in the Initial System
The diagram below shows how the initial system handled reads and writes to the viewing history data.
Every time a user started watching a show or movie, Netflix added a new column to their viewing history record in the database. If the user paused or stopped watching, that same column was updated to reflect their latest progress.
While storing data was easy, retrieving it efficiently became more challenging as users' viewing histories grew. Netflix used three different methods to fetch data, each with its advantages and drawbacks:
Retrieving the Entire Viewing History: If a user had only watched a few shows, Netflix could quickly fetch their entire history in one request. However, for long-time users with large viewing histories, this approach became slow and inefficient as more data accumulated.
Searching by Time Range: Sometimes, Netflix only needed to fetch records from a specific time period, like a user’s viewing history from the last month. While this method worked well in some cases, performance varied depending on how many records were stored within the selected time range.
Using Pagination for Large Histories: To avoid loading huge amounts of data at once, Netflix used pagination. This approach prevented timeouts (when a request takes too long and fails), but it also increased the overall time needed to retrieve all records.
At first, this system worked well because it provided a fast and scalable way to store viewing history. However, as more users watched more content, this system started to hit performance limits. Some of the issues were as follows:
Too Many SSTables: Apache Cassandra® stores data in files called SSTables (Sorted String Tables). Over time, the number of these SSTables increased significantly. For every read, the system had to scan multiple SSTables across the disk, making the process slower.
Compaction Overhead: Apache Cassandra® performs compactions to merge multiple SSTables into fewer, more efficient ones. With more data, these compactions took longer and required more processing power. Other operations like read repair and full column repair also became expensive.
To speed up data retrieval, Netflix introduced a caching solution called EVCache.
Instead of reading everything from the Apache Cassandra® database every time, each user’s viewing history is stored in a cache in a compressed format. When a user watches a new show, their viewing data is added to Apache Cassandra® and merged with the cached value in EVCache. If a user’s viewing history is not found in the cache, Netflix fetches it from Cassandra®, compresses it, and then stores it in EVCache for future use.
By adding EVCache, Netflix significantly reduced the load on their Apache Cassandra® database. However, this solution also had its limits.
The New Approach: Live & Compressed Storage Model
One important fact about the viewing history data was this: most users frequently accessed only their recent viewing history, while older data was rarely needed. However, storing everything the same way led to unnecessary performance and storage costs.
To solve this, Netflix redesigned its storage model by splitting viewing history into two categories:
Live Viewing History (LiveVH): It stores recently watched content that users access frequently. It is also uncompressed for fast reads and writes. This is designed for quick updates, like when a user pauses or resumes a show.
Compressed Viewing History (CompressedVH): Stores older viewing records that are accessed less frequently. It is compressed to save storage space and improve performance.
Since LiveVH and CompressedVH serve different purposes, they were tuned differently to maximize performance.
For LiveVH, which stores recent viewing records, Netflix prioritized speed and real-time updates. Frequent compactions were performed to clean up old data and keep the system running efficiently. Additionally, a low GC (Garbage Collection) grace period was set, meaning outdated records were removed quickly to free up space. Since this data was accessed often, frequent read repairs were implemented to maintain consistency, ensuring that users always saw accurate and up-to-date viewing progress.
On the other hand, CompressedVH, which stores older viewing records, was optimized for storage efficiency rather than speed. Since this data was rarely updated, fewer compactions were needed, reducing unnecessary processing overhead. Read repairs were also performed less frequently, as data consistency was less critical for archival records. The most significant optimization was compressing the stored data, which drastically reduced the storage footprint while still making older viewing history accessible when needed.
The Large View History Performance Issue
Even with compression, some users had extremely large viewing histories. For these users, reading and writing a single massive compressed file became inefficient.
If CompressedVH grows too large, retrieving data becomes slow. Also, single large files create performance bottlenecks when read or written. To avoid these issues, Netflix introduced chunking, where large compressed data is split into smaller parts and stored across multiple Apache Cassandra® database nodes.
Here’s how it works:
Instead of storing everything in one large file, Netflix stores metadata that tracks data version and chunk count.
Instead of writing one large file, each chunk is written separately. A metadata entry keeps track of all the chunks, ensuring efficient storage.
Metadata is read first, so Netflix knows how many chunks to retrieve. Chunks are fetched in parallel, significantly improving read speeds.
This system gave Netflix the headroom needed to handle future growth.
The New Challenges
With the global expansion of Netflix, the company launched its service in 130+ new countries and introduced support for 20 languages. This led to a massive surge in data storage and retrieval needs.
At the same time, Netflix introduced video previews in the user interface (UI), a feature that allowed users to watch short clips before selecting a title. While this improved the browsing experience, it also dramatically increased the volume of time-series data being stored.
When Netflix analyzed the performance of its system, it found several inefficiencies:
The storage system did not differentiate between different types of viewing data. Video previews and full-length plays were stored identically, despite having very different usage patterns.
Language preferences were stored repeatedly across multiple viewing records, leading to unnecessary duplication and wasted storage.
Since video previews generated many short playback records, they accounted for 30% of the data growth every quarter.
Netflix’s clients (such as apps and web interfaces) retrieved more data than they needed. Most queries only required recent viewing data, but the system fetched entire viewing histories regardless. Because filtering happened after data was fetched, huge amounts of unnecessary data were transferred across the network, leading to high bandwidth costs and slow performance.
Read latencies at the 99th percentile (worst-case scenarios) became highly unpredictable. Some queries were fast, but others took too long to complete, causing an inconsistent user experience.
At this stage, Netflix’s existing architecture could no longer scale efficiently.
Apache Cassandra® had been a solid choice for scalability, but by this stage, Netflix was already operating one of the largest Apache Cassandra® clusters in existence. The company had already pushed Apache Cassandra® to its limits, and without a new approach, performance issues would continue to worsen as the platform grew.
A more fundamental redesign was needed.
Netflix’s New Storage Architecture
To overcome the growing challenges of data overload, inefficient retrieval, and high latency, Netflix introduced a new storage architecture that categorized and stored data more intelligently.
Step 1: Categorizing Data by Type
One of the biggest inefficiencies in the previous system was that all viewing data (whether it was a full movie playback, a short video preview, or a language preference setting) was stored together.
Netflix solved this by splitting viewing history into three separate categories, each with its dedicated storage cluster:
Full Title Plays: Data related to complete or partial streaming of movies and TV shows. This is the most critical data because it affects resume points, recommendations, and user engagement metrics.
Video Previews: Short clips that users watch while browsing content. Since this data grows quickly but isn’t as important as full plays, it required a different storage strategy.
Language Preferences: Information on which subtitles or audio tracks a user selects. This was previously stored redundantly across multiple viewing records, wasting storage. Now, it is stored separately and referenced when needed.
Step 2: Sharding Data for Better Performance
Netflix sharded (split) its data across multiple clusters based on data type and data age, improving both storage efficiency and query performance.
See the diagram below for reference:
1 - Sharding by Data Type
Each of the three data categories (Full Plays, Previews, and Language Preferences) was assigned its separate cluster.
This allowed Netflix to tune each cluster differently based on how frequently the data was accessed and how long it needed to be stored. It also prevented one type of data from overloading the entire system.
2 - Sharding by Data Age
Not all data is accessed equally. Users frequently check their recent viewing history, but older data is rarely needed. To optimize for this, Netflix divided its data storage into three time-based clusters:
Recent Cluster (Short-Term Data): Stores viewing data from the past few days or weeks. Optimized for fast reads and writes since recent data is accessed most frequently.
Past Cluster (Archived Data): Holds viewing records from the past few months to a few years. Contains detailed records, but is tuned for slower access since older data is less frequently requested.
Historical Cluster (Summarized Data for Long-Term Storage): Stores compressed summaries of viewing history from many years ago. Instead of keeping every detail, this cluster only retains key information, reducing storage size.
Optimizations in the New Architecture
To keep the system efficient, scalable, and cost-effective, the Netflix engineering team introduced several key optimizations.
1 - Improving Storage Efficiency
With the introduction of video previews and multi-language support, Netflix had to find ways to reduce the amount of unnecessary data stored. The following strategies were adopted:
Many previews were only watched for a few seconds, which wasn’t a strong enough signal of user interest. Instead of storing every short preview play, Netflix filtered out these records before they were written to the database, significantly reducing storage overhead.
Previously, language preference data (subtitles/audio track choices) was duplicated across multiple viewing records. This was inefficient, so Netflix changed its approach. Now, each user's language preference is stored only once, and only changes (deltas) are recorded when the user selects a different language.
To keep the database from being overloaded, Netflix implemented Time-To-Live (TTL)-based expiration, meaning that unneeded records were deleted automatically after a set period.
Netflix continued using two separate storage methods to store recent data in an uncompressed format and older data in compressed format.
2 - Making Data Retrieval More Efficient
Instead of fetching all data at once, the system was redesigned to retrieve only what was needed:
Recent data requests were directed to the Recent Cluster to support low-latency access for users checking their recently watched titles.
Historical data retrieval was improved with parallel reads. Instead of pulling old records from a single location, Netflix queried multiple clusters in parallel, making data access much faster.
Records from different clusters were intelligently stitched together. Even when a user requested their entire viewing history, the system combined data from various sources quickly and efficiently.
3 - Automating Data Movement with Data Rotation
To keep the system optimized, Netflix introduced a background process that automatically moved older data to the appropriate storage location:
Recent to Past Cluster: If a viewing record was older than a certain threshold, it was moved from the Recent Cluster to the Past Cluster. This ensured that frequently accessed data stayed in a fast-access storage area, while older data was archived more efficiently.
Past to Historical Cluster: When data became older, it was summarized and compressed, then moved to the Historical Cluster for long-term storage with minimal resource usage.
Parallel writes & chunking were used to ensure that moving large amounts of data didn’t slow down the system.
See the diagram below:
4 - Caching for Faster Data Access
Netflix restructured its EVCache (in-memory caching layer) to mirror the backend storage architecture.
A new summary cache cluster was introduced, storing precomputed summaries of viewing data for most users. This meant that instead of computing summaries every time a user made a request, Netflix could fetch them instantly from the cache. They managed to achieve a 99% cache hit rate, meaning that nearly all requests were served from memory rather than querying Apache Cassandra®, reducing overall database load.
Conclusion
With the growth that Netflix went through, their engineering team had to evolve the time-series data storage system to meet the increasing demands.
Initially, Netflix relied on a simple Apache Cassandra®-based architecture, which worked well for early growth but struggled as data volume soared. The introduction of video previews, global expansion, and multilingual support pushed the system to its breaking point, leading to high latency, storage inefficiencies, and unnecessary data retrieval.
To address these challenges, Netflix redesigned its architecture by categorizing data into Full Title Plays, Video Previews, and Language Preferences and sharding data by type and age. This allowed for faster access to recent data and efficient storage for older records.
These innovations allowed Netflix to scale efficiently, reducing storage costs, improving data retrieval speeds, and ensuring a great streaming experience for millions of users worldwide.
Note: Apache Cassandra® is a registered trademark of the Apache Software Foundation.
References:
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