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by "New Relic" <emeamarketing@newrelic.com> - 04:31 - 19 Dec 2024 -
How to promote your business
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by "Matt Budd" <matt@infoserve.uk> - 02:15 - 19 Dec 2024 -
These industries could transform the world economy by 2040
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 10:07 - 18 Dec 2024 -
It’s not enough to just build AI. Companies should also ensure it’s trustworthy AI.
Re:think
FRESH TAKES ON BIG IDEAS
ON AI TRUST
Why investing in AI trust pays offIt’s becoming increasingly clear that AI can bring superpowers to people in many roles and will change how they work. Soon, all software that companies build and buy will become AI software, which will be more flexible and adaptive than today’s rule-based systems. This will shrink time from opportunity discovery to action. Integration will get easier as agents dynamically bridge systems so that fragmented business processes come together in interconnected flows. The shift will unlock immense value for enterprises of all kinds, enhancing velocity, productivity, and innovation.
But to get there, trust is critical. Stakeholder expectations are rising as perceived risks also rise, and that’s leading to rapid experimentation with new regulation. But the regulatory patterns and postures around the world are far from settled. And while compliance is essential, building AI trust goes well beyond compliance.
The companies that derive the most value from AI will be those that create trust with their customers, employees, and stakeholders. Fundamentally, people must trust AI enough to hand over tasks. Enhanced evaluations, transparency, and explainability can all contribute—as well as flexible governance that puts principles into practice while encouraging innovation. Organizations can start with a principled approach to deciding not just what they can build but what they should build. These ethical decisions must be rooted in the values unique to each organization and the values of a society that places humans at the center of the AI ecosystem. This approach to building trust is responsible AI, or RAI. And when implemented well, RAI leads to real ROI.
Our research has found that the majority of large companies (72 percent) are implementing AI today in at least one business process, but just 18 percent have an RAI council with decision authority. Making AI governance work requires bringing people together who can offer complementary cross-functional perspectives.
Getting RAI right means implementing guidelines for all and operationalizing formal AI trust policies. This can create a psychologically safe environment where employees feel empowered to innovate boldly. But organizations also need technical guardrails to ensure their AI systems can be driven fast but safely. Across industries, we have repeatedly seen how the right AI guardrails can accelerate innovation, not impede it.
Trusted data is also key to AI innovation. AI builders should constantly ask themselves: “How do we create the right metadata to track the provenance of data sets, how they were collected, and how they therefore can be used?” Deploying a data operating guide ensures that AI builders have well-curated and documented data for responsible innovation.“The majority of large companies are implementing AI today in at least one business process, but just 18 percent have a responsible AI council with decision authority.”
In parallel with strengthening data governance and guardrails, leaders must tackle the hard work of building and implementing trusted AI deployment processes. I often advise leaders to take three steps to move fast.
First, educate the enterprise. Create a clear communication plan about what AI trust means for the entire organization and why everyone should be committed to it. Define how executives should lead in an AI era—and then roll out structured reskilling and upskilling programs. Even top technologists can be new to many aspects of RAI and will need to learn new human-centered AI engineering practices.
Second, invest in AI trust. Allocating the right resources requires treating it as an asset that is built up and not a cost of compliance to be “managed down” in the presence of regulatory scrutiny. This means creating a multiquarter road map for enhancing RAI maturity that embraces people, processes, and technology in a well-orchestrated action plan.
Third, engage cross-functional teams to deploy a strong governance platform, including registries for the software and the data resources that must be built or bought, as well as end-to-end workflows to ensure the right controls are in place. In addition, at the model and product level, AI tools should be continuously monitored by machine learning operations, or MLOps, for performance, quality, and risk. These “engine room” technologies are critical to ensuring that leaders “on the bridge” can make confident decisions.
Getting AI trust right is a shared responsibility between the organizations deploying AI and the platform providers, governments, international organizations, and standards bodies aiming to ensure that AI is safe and reliable. In this dynamic environment, academic researchers, open-source communities, and developers also play a big role in building AI that is more trustworthy, transparent, and explainable. CEOs and chief technology officers can do their part by getting their data houses in order, empowering their teams to innovate safely, and monitoring all their AI deployments for signs of bias or misinformation.Share Roger Roberts’ insights
ABOUT THIS AUTHOR
Roger Roberts is a partner in McKinsey’s Bay Area office.
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by "McKinsey Quarterly" <publishing@email.mckinsey.com> - 01:11 - 18 Dec 2024 -
What leading UK companies do to thrive
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Positive economic indicators. Although the UK’s FTSE hasn’t performed as strongly as some other global stock exchanges, McKinsey senior partners Andrew Goodman and Tunde Olanrewaju note that the UK economy still has a lot of bright spots. In an episode of The McKinsey Podcast, they cite the amount of private capital flowing into the UK market as one such example. In fact, over the past 15 years, the UK has attracted about $1.8 trillion in private equity investments and nearly $1 trillion in foreign direct investment.
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 12:48 - 18 Dec 2024 -
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by "McKinsey Quarterly Five Fifty" <publishing@email.mckinsey.com> - 03:30 - 17 Dec 2024 -
How Statsig Streams 1 Trillion Events A Day
How Statsig Streams 1 Trillion Events A Day
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The newsletter is a collaboration between ByteByteGo and members of the Statsig engineering team (Pablo Beltran and Brent Echols).
Statsig is a modern feature management, experimentation, and analytics platform that enables teams to improve their product velocity.
Over the past year, Statsig has seen a staggering 20X growth in its event volume. This growth has been driven by high-profile customers such as OpenAI, Atlassian, Flipkart, and Figma, who rely on Statsig’s platform for experimentation and analytics.
Statsig currently processes over a trillion events daily, which is a remarkable achievement for any organization, particularly one of their size as a startup.
Source: Statsig Technical Blog However, this rapid growth comes with immense challenges.
Statsig must not only scale its infrastructure to handle the sheer volume of data but also ensure that its systems remain reliable and maintain high uptime. It also needs to keep the costs in check to stay competitive.
In this post, we’ll look at Statsig’s streaming architecture, which helps it handle the event volume. We’ll also look at the cost-efficiency steps taken by Statsig’s team.
Statsig’s Streaming Architecture
On a high level, Statsig’s streaming architecture consists of 3 main components:
Request Recorder: This stage of Statsig’s pipeline is designed with one main priority: ensuring that no data is ever lost. The top goal here is to make sure that every piece of data gets captured and stored, no matter what.
Log Processing: In this stage, the raw data collected during request recording gets refined and prepared for use. This stage applies specific rules and logic to the raw data while also ensuring that the data is accurate.
Routing: This stage acts like a smart traffic controller for data. Once the data has been recorded and processed, this stage ensures it reaches the right destination. It allows the pipeline to handle varied customer requirements without creating separate systems for each use case.
See the diagram below for a quick overview of the architecture:
The Architectural Components
While the high-level view gives a broad look at Statsig’s pipeline architecture, let us also look at each component in more detail to gain a better understanding.
1 - Data Ingestion Layer
The data ingestion layer in Statsig's pipeline is the first and one of the most crucial stages of the system.
It is responsible for receiving, authenticating, organizing, and securely storing data in a way that prevents loss, even under challenging conditions.
The request recorder is a key functionality of the data ingestion layer, with its specific role being the first step in handling incoming data. However, the data ingestion layer includes the load balancer, authentication, rebatching, and persistence-related functionalities.
See the diagram below:
Here’s a simple breakdown of the various steps:
From Client SDKs to Load Balancer:
The process starts when data is sent from client SDKs. This data is routed through a load balancer.
The load balancer ensures that incoming requests are evenly distributed across servers to avoid overloading any single server. This helps maintain system stability and performance.
Service Endpoint & Authorization:
Once the data reaches the server, it is passed to a service endpoint where the system performs authentication. This step ensures that only valid and authorized data batches are processed.
After authentication, the data is added to an internal queue. This queue holds the data temporarily until it can be processed further, ensuring no data is lost even if the processing system experiences delays.
Rebatching for Efficiency:
At this stage, smaller data batches are combined into larger ones, a process called rebatching. Handling fewer, larger batches is more efficient than dealing with many small ones, both in terms of performance and cost.
Statsig uses a dynamic configuration system to determine the optimal size for these batches.
The rebatching process then writes the optimized data batches to Google Cloud Storage (GCS). Using GCS for this purpose is a cost-effective solution compared to directly streaming smaller batches through high-cost pipelines.
Persistence for Reliability:
After rebatching, the data is securely stored in a persistence layer, which serves as a semi-permanent storage system. This layer is designed with multiple fallback options, meaning it has backup mechanisms to store data in case the primary system encounters issues.
The processing units, known as pods, are equipped with sufficient memory to handle temporary failures. This ensures the system can continue operating for up to an hour during an outage without losing data or affecting performance.
These fallback mechanisms extend across different regions and cloud platforms, providing an extra layer of reliability to the system.
2 - Message Queue Layer
The Message Queue Layer is a critical stage in Statsig’s pipeline that manages how data flows between different components. This layer is designed to handle enormous volumes of data efficiently while keeping operational costs low.
See the diagram below:
As you can see, it consists of two main components:
The Pub/Sub Topic
Pub/Sub is a serverless messaging system that facilitates communication between different parts of the pipeline. Since it is serverless, there’s no need to worry about maintaining servers or managing complex deployments. This reduces overhead for the engineering team.
Pub/Sub receives metadata about the data stored in Google Cloud Storage (GCS). Instead of directly storing all the event data, it acts as a pointer system, referring downstream systems to the actual data stored in GCS.
GCS Bucket
Directly using Pub/Sub for all data storage would be prohibitively expensive. Therefore, Statsig offloads most of the data to GCS to reduce storage and operational costs.
The pipeline writes bulk data into GCS in compressed batches. Pub/Sub stores only the metadata (like file pointers) needed to locate this data in GCS. Downstream components can then use these pointers to retrieve the data when required.
The GCS Bucket stores the actual data in a compressed format, using Zstandard (ZSTD) compression for efficiency. For reference, Zstandard compression is highly efficient, providing better compression rates (around 95%) than other methods like zlib, with lower CPU usage. This ensures data is stored in a smaller footprint while maintaining high processing speeds.
3 - Business Logic Layer
The Business Logic Layer is where the heavy lifting happens in Statsig's pipeline.
This layer is designed to process data while ensuring accuracy and preparing it for final use by various downstream systems. It handles complex logic, customization, and data formatting.
See the diagram below that shows the various steps that happen in this layer:
Let’s look at each step in more detail:
Rebatching
This step combines smaller batches of incoming data into larger ones for processing. By handling larger batches, the system reduces the overhead of dealing with multiple small data chunks.
The system is designed with an “at least once” guarantee. This means that even if something goes wrong during processing, the data is not lost. It will be retried until successfully processed.
Stateful Processing To Remove Redundancy
This step focuses on deduplication, which involves filtering out repeated or redundant data. For instance, if the same event gets recorded multiple times, this step ensures only one instance is kept.
To achieve this, the system uses caching solutions like Memcached. Memcached provides quick access to previously processed data, enabling the system to identify duplicates efficiently.
Ultimately, deduplication reduces unnecessary processing.
Business Logic Plugins
This layer allows different teams within Statsig to insert custom business logic tailored to their specific needs. For example, one team might add specific tags or attributes to the data, while another might modify event structures for a particular customer.
By using plugins, the system can support diverse use cases without requiring a separate pipeline for each customer. This makes the pipeline both scalable and versatile.
Writer
Once the data has been cleaned, transformed, and customized, the Writer finalizes it by writing it to the appropriate destination.
This could be a database, a data warehouse, or an analytics tool, depending on where the data is needed.
4 - Routing and Integration Layer
The routing and integration layer in Statsig's pipeline is responsible for directing processed data to its final destination.
See the diagram below:
Let’s look at each branch in more detail:
Warehouse Router
The Warehouse Router is responsible for deciding where the data should go based on factors like customer preferences, event types, and priority. It dynamically routes data to various destinations such as BigQuery or other data warehouses.
Here’s how it works:
For data that does not require immediate access (latency-insensitive), the router uses load jobs. These batch jobs process and upload data in bulk, significantly reducing costs compared to real-time streaming.
However, for critical, time-sensitive data, the router leverages the storage write API to deliver data in real-time, ensuring low latency but at a higher cost.
The Warehouse Router guarantees efficient resource utilization by distinguishing between latency-sensitive and latency-insensitive data. It saves costs without compromising on performance for urgent tasks.
The Side Effects Service
This service handles external integrations triggered by specific events in the pipeline. For example:
Sending data to third-party systems via webhooks.
Triggering actions or workflows in external platforms based on predefined rules.
It supports any kind of event-level trigger, making it highly customizable for customer-specific workflows.
The Real-Time Event Stream
This service is designed for situations where data needs to be accessed almost instantaneously. For example:
Real-time dashboards that display user activity.
Monitoring tools that require immediate updates.
It uses Redis, a fast in-memory data store, to cache and retrieve data in real-time so that customers querying the data experience minimal delays.
The Shadow Pipeline
The Shadow Pipeline is an important testing feature in Statsig’s event streaming system.
It acts as a safety net to ensure that any updates or changes to the system don’t disrupt its ability to process over a trillion events a day.
Here’s a closer look at how the shadow pipeline works:
Parallel Testing Environment: The shadow pipeline runs alongside the main production pipeline but doesn’t affect live data or customer systems. It mirrors the production setup, processing the same kinds of data with a new version of the software (release candidate). This allows engineers to test updates in a controlled environment without risking disruptions to the main pipeline.
Simulating Real Traffic: To test the shadow pipeline’s performance, a couple of million events are sent through it in a large burst. This mimics the high-traffic conditions the system handles daily, providing a realistic scenario for evaluation.
Comparing Results: The outputs of the shadow pipeline are carefully compared with those from the production pipeline. Automated checks look for discrepancies, such as missing or incorrect data, and alert engineers if anything falls outside expected tolerances.
Stress Testing Infrastructure: The traffic burst also tests the system’s ability to handle sudden spikes in data volume. It evaluates features like horizontal pod autoscaling (HPA) that automatically adjusts computing resources to match demand.
Statsig’s Cost Optimization Strategies
Statsig employed multiple cost optimization strategies to handle the challenge of processing over a trillion events daily while keeping operational expenses as low as possible.
These strategies involve a mix of technical solutions, infrastructure choices, and design decisions.
Let’s break down each key effort in more detail:
GCS Upload via Pub/Sub
Instead of sending all event data directly into Pub/Sub, Statsig writes the majority of the data to Google Cloud Storage (GCS) in a compressed format.
Using GCS is significantly cheaper than relying solely on Pub/Sub for storing large amounts of data. It helps reduce costs while maintaining scalability. Pub/Sub is used only to pass file pointers (metadata) that direct downstream systems to retrieve the data from GCS.
Async Workloads on Spot Nodes
Statsig runs non-time-sensitive tasks (asynchronous workloads) on spot nodes, which are temporary virtual machines offered at a lower price.
Leveraging spot nodes reduces VM costs without compromising performance for less urgent processes. Also, since these workloads don’t require constant uptime, occasional interruptions don’t impact the system’s overall functionality.
Deduplication with Memcache
A large portion of incoming events may include duplicates, which add unnecessary processing overhead. Deduplication is a key feature that saves processing resources and ensures downstream systems only handle unique data.
To handle deduplication, Statsig uses Memcache to identify and discard duplicate events early in the pipeline.
Zstandard (zstd) Compression
Statsig switched from using zlib compression to ZSTD, a more efficient compression algorithm.
ZSTD achieves better compression rates (around 95%) while using less CPU power, compared to zlib’s 90% compression. This improvement reduced storage requirements and processing power.
Batching Efficiency via CPU Optimization
Statsig also adjusted the CPU allocation for its request recorder (from 2 CPU to 12 CPU), enabling it to handle larger batches of data more efficiently.
This is because larger batches reduce the number of write operations to storage systems, improving cost efficiency while maintaining high throughput.
Load Jobs vs. Live Streaming
For data that doesn’t need to be processed immediately, Statsig uses load jobs to process and upload data in bulk, which is much cheaper.
On the other hand, for time-sensitive data, they use the storage write API, which provides low-latency delivery but at a higher cost.
Differentiating between these two types of data saves money while meeting customer requirements for both real-time and batch processing.
Optimized CPU and Memory Utilization
Statsig tunes CPU and memory usage based on actual host utilization rather than pod utilization.
Also, pods are configured without strict usage limits, allowing them to make full use of available resources when needed. This prevents underutilization of expensive hardware resources and maximizes cost-effectiveness.
Aggressive Host-Level Resource Stacking
Statsig stacks multiple pods onto a single host aggressively to use every bit of available CPU and memory.
By fine-tuning flow control and concurrency settings, they prevent resource contention while maintaining high performance. This approach helps achieve cost efficiency at the host level by reducing the number of machines needed.
Conclusion
Statsig’s journey to streaming over a trillion events daily shows how a company can achieve massive scale without compromising efficiency through innovative engineering.
By designing a robust data pipeline with key components like a reliable ingestion layer, scalable message queues, and cost-optimized routing and integration layers, Statsig has built an infrastructure capable of supporting rapid growth while maintaining high reliability and performance. Also, leveraging features and tools like Pub/Sub, GCS, and advanced compression techniques, the platform balances the challenges of low latency, data integrity, and cost-effectiveness.
A key differentiator for Statsig is its approach to cost optimization and scalability, achieved through strategies such as using spot nodes, implementing deduplication, and differentiating latency-sensitive from latency-insensitive workloads. These efforts not only ensure the system's resilience but also allow them to offer their platform at competitive prices to a wide range of customers.
Reference:
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by "ByteByteGo" <bytebytego@substack.com> - 10:36 - 17 Dec 2024 -
Reminder: 2025 predictions for intelligent ERP
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by "SAP" <sap@mailsap.com> - 07:22 - 17 Dec 2024 -
How did your year-end review go?
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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.
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Performance management that motivates. What actually inspires employees to perform at their best? According to McKinsey talent experts Brooke Weddle and Bryan Hancock, today’s workers prioritize clarity and simplicity in their evaluations. In short, employees want to know what managers are holding them accountable for. They also prefer streamlined performance management systems that make it easy to understand how they measure up, as well as how performance factors are weighed and assessed.
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Improving feedback discussions. The role of manager is critical not only for evaluating employees and delivering feedback but also for helping employees apply feedback in a meaningful way. Yet according to our most recent research, more than one-third of respondents at large companies said their managers lack the skills to have these development conversations. Listen to the full episode of McKinsey Talks Talent for more tips on making feedback discussions more constructive.
—Edited by Belinda Yu, editor, Atlanta
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Navigating changes in organizational health: A leader’s guide
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.
—Alex and Axel
Good organizational health is the best predictor of long-term success. But managing its key elements—a company’s culture, behavior, and management practices—is becoming more challenging. New technologies, evolving workplace norms, and other disruptions are forcing companies to fine-tune how they operate and how they manage their people—both of which are critical to improving their health and their bottom line. For leaders, this can make their jobs more complicated and demanding than ever. This week, we look at ways to manage the constant changes that are affecting organizational health.
Over two-plus decades, McKinsey’s Organizational Health Index (OHI) has helped leaders identify which practices drive the strongest performance—and recognize if their organizations have any of them in place. The latest OHI results highlight six practices reflecting the areas of most profound change in these fast-moving times: purpose, leadership, decision making, employee experience, technology, and social responsibility. “How leaders navigate these changes may well dictate whether their organizations simply survive or truly thrive,” say McKinsey senior partners Aaron De Smet and Arne Gast and their coauthors. For example, leaders can enhance employee engagement and raise productivity by clearly articulating the organization’s “why”—that is, its fundamental purpose—rather than simply explaining what the strategy is and how to execute it. “Our new research found that this involvement can be shallow and transactional when it isn’t tied to a deeper sense of purpose and meaning,” the authors note.
That’s how much more likely companies are to be healthy when they are run by decisive leaders, according to McKinsey’s OHI research. McKinsey’s Alex Camp, Arne Gast, Brooke Weddle, and Drew Goldstein say that making quick decisions and committing to them is a more effective style than authoritative leadership, which is based on using authority, pressure, and influence to get things done. Decisive and empowering leadership became increasingly important after the COVID-19 pandemic, which put more pressure on organizations to transform themselves swiftly. “Decisive leadership is not just for times of crisis, however; it’s a requirement for any business that just wants to keep up,” the authors say.
That’s McKinsey’s Bryan Hancock on how organizational health is shifting in the new world of work. In an episode of the McKinsey Talks Talent podcast, Hancock and McKinsey’s Brooke Weddle note that putting more data in the hands of frontline employees can help foster more innovation, which is one of the OHI’s nine key outcomes. That practice requires a strong process for collecting and evaluating the best ideas from people at all levels of the organization. “A lot of times, a frontline employee on the ground can say, ‘Here’s five ideas for how this could go better,’” Weddle says. “Organizations that listen, in a structured way, can be very effective at driving that continuous improvement.”
Putting employees first and treating them with the same care given to customers will help companies become healthier, Columbia University business professor Stephan Meier says in a McKinsey Author Talks interview. “The 100-plus years of scientific management demonstrate that we have thought about workers as cogs in a machine and not really as human beings,” he says. “Businesses have to treat workers differently and humanize them in order to thrive.” Just as companies prioritize customer service and care, they can embrace a similar approach for employees—offering more personalized support and dismissing the outdated mindset that employees are motivated by money alone. “Customer centricity is not only about lowering the price. It’s about improving the customer experience,” Meier says. “Similarly, employee centricity is not only about increasing the wage. It’s much more about increasing the employee experience.”
Lead by nurturing your organization’s health.
— Edited by Eric Quiñones, senior editor, New York
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Copyright © 2024 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007
by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 03:10 - 16 Dec 2024 -
How can successful leaders upgrade their ways of working?
Only 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
•
Your personal operating model. Leaders likely understand the importance of updating their devices’ operating systems, which can help those tools run more smoothly. What many executives may not have considered, though, is how critical it is to update their own operating systems. The personal operating model—comprising one’s priorities, roles, and time and energy allocations—defines how leaders get work done and ultimately determines that person’s level of success, McKinsey senior partner Arne Gast and his colleague share.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 12:42 - 16 Dec 2024 -
The year's most popular articles from McKinsey Global Institute
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As 2024 draws to a close, we’re revisiting our most popular insights of the year. Today, we give you our best-read pieces from the McKinsey Global Institute, which focus on topics including navigating the energy transition, investing in productivity growth, and more. At No. 1 is "Geopolitics and the geometry of global trade," by McKinsey’s Jeongmin Seong, Olivia White, Michael Birshan, and coauthors. Read on for our full top 10, and don't miss McKinsey Publishing's "Meet the moment"—our best insights from 2024 to help you navigate change.
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by "McKinsey Top Ten" <publishing@email.mckinsey.com> - 02:26 - 15 Dec 2024 -
The week in charts
The Week in Charts
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Copyright © 2024 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007
by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 02:20 - 14 Dec 2024 -
A proactive approach to navigating geopolitics
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Geopolitical tensions are no longer a distant threat; they’re a daily reality affecting business decisions. CEOs today view geopolitical tensions as the biggest risk to economic growth, write McKinsey’s Cindy Levy, Shubham Singhal, and Matt Watters in this month’s featured story. A proactive approach can help companies both safeguard existing operations and capture emerging opportunities in various geographical and industry segments. Other highlights include the following topics:
•
how CEOs can safely steer their organizations’ performance to new heights
•
what’s ahead for the fashion industry
•
the four drivers of the personal operating model
•
how insurers can capture profitable growth as they navigate a shifting landscape
The CEO’s essential checklist: Questions every chief executive should be able to answer
Most great CEOs rely on a set of guiding mindsets. This checklist helps them turn mindsets into practices that can steer their companies to great heights.
Turn mindsets into practices
The State of Fashion 2025: Challenges at every turn
The global fashion industry faces economic uncertainty, a dynamic market, and consumer behavior shifts. Finding pockets of growth means navigating a complex maze.
Identify the bright spots
Warning: Upgrade your personal operating model
Effective leaders continually adapt their priorities, roles, time, and energy practices to stay ahead of new realities. Here’s why you need to do the same.
4 drivers
Global Insurance Report 2025: The pursuit of growth
Finding profitable growth is an imperative for the world’s personal and commercial property and casualty insurers, while life carriers must adjust to changing consumer needs.
Embrace a changing landscape
Digital twins: The key to unlocking end-to-end supply chain growth
Complexity, COVID-19, and competition have upended supply chain organizations. They can reignite revenue growth and deepen resilience by deploying AI-powered digital twins.
5 key tenets for success
Why being in HR is getting tougher—and how to break through
You’re not imagining it: the past five years have taken a toll on human resources. Here’s how leaders can help.
Boost morale
McKinsey Explainers
Find direct answers to complex questions, backed by McKinsey’s expert insights.
Learn more
McKinsey Themes
Browse our essential reading on the topics that matter.
Get up to speed
McKinsey on Lives & Legacies
Monthly obituaries from business and society, highlighting the lasting legacies of executives and leaders from around the globe.
Explore the latest obituaries
The Weekend Read
McKinsey Classics
Most digital strategies don’t reflect how digital is changing economic fundamentals, industry dynamics, or what it means to compete. Read our 2018 classic “Why digital strategies fail” to learn more.
Rewind
Mind the Gap
Biweekly curated reads on Gen Z in the workplace.
Subscribe now— Edited by Eleni Kostopoulos, managing editor, New York
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by "McKinsey Highlights" <publishing@email.mckinsey.com> - 11:09 - 14 Dec 2024 -
EP142: The Fundamental Pillars of Object-Oriented Programming
EP142: The Fundamental Pillars of Object-Oriented Programming
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This week’s system design refresher:
Apache Kafka Fundamentals You Should Know (Youtube video)
The Fundamental Pillars of Object-Oriented Programming
Top 6 Multithreading Design Patterns You Must Know
How Do C++, Java, Python Work?
Explaining 9 types of API testing
REST API Vs. GraphQL
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Apache Kafka Fundamentals You Should Know
The Fundamental Pillars of Object-Oriented Programming
Abstraction, Encapsulation, Inheritance, and Polymorphism are the four pillars of object-oriented programming. What do they mean?
Abstraction
This is the process of hiding implementation details and showing only the essential features of an object. For example, a Vehicle class with an abstract stop method.Encapsulation
It involves wrapping data (fields) and methods in a single unit (class) and restricting direct access using access modifiers. For example, private fields with public getters and setters.Inheritance
The process of creating a new class (child) that inherits attributes and methods from an existing class (parent), thereby promoting code reuse. For example, a Car class inherits from a Vehicle class.Polymorphism
It allows methods to perform differently based on the object they are invoked on. When two types share an inheritance chain, they can be used interchangeably with no errors.
Over to you: Do you use object-oriented programming?
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Top 6 Multithreading Design Patterns You Must Know
Multithreading enables a single program or process to execute multiple tasks concurrently. Each task is a thread. Think of threads as lightweight units of execution that share the resources of the process such as memory space.
However, multithreading also introduces complexities like synchronization, communication, and potential race conditions. This is where patterns help.Producer-Consumer Pattern
This pattern involves two types of threads: producers generating data and consumers processing that data. A blocking queue acts as a buffer between the two.Thread Pool Pattern
In this pattern, there is a pool of worker threads that can be reused for executing tasks. Using a pool removes the overhead of creating and destroying threads. Great for executing a large number of short-lived tasks.Futures and Promises Pattern
In this pattern, the promise is an object that holds the eventual results and the future provides a way to access the result. This is great for executing long-running operations concurrently without blocking the main thread.Monitor Object Pattern
Ensures that only one thread can access or modify a shared resource within an object at a time. This helps prevent race conditions. The pattern is required when you need to protect shared data or resources from concurrent access.Barrier Pattern
Synchronizes a group of threads. Each thread executes until it reaches a barrier point in the code and blocks until all threads have reached the same barrier. Ideal for parallel tasks that need to reach a specific stage before starting the next stage.Read-Write Lock Pattern
It allows multiple threads to read from a shared resource but only allows one thread to write to it at a time. Ideal for managing shared resources where reads are more frequent than writes.
Over to you: Which other multithreading design pattern will you add to the list?
How Do C++, Java, Python Work?
The diagram shows how the compilation and execution work.
Compiled languages are compiled into machine code by the compiler. The machine code can later be executed directly by the CPU. Examples: C, C++, Go.
A bytecode language like Java, compiles the source code into bytecode first, then the JVM executes the program. Sometimes JIT (Just-In-Time) compiler compiles the source code into machine code to speed up the execution. Examples: Java, C#
Interpreted languages are not compiled. They are interpreted by the interpreter during runtime. Examples: Python, Javascript, Ruby
Compiled languages in general run faster than interpreted languages.Explaining 9 types of API testing
Smoke Testing
This is done after API development is complete. Simply validate if the APIs are working and nothing breaks.Functional Testing
This creates a test plan based on the functional requirements and compares the results with the expected results.Integration Testing
This test combines several API calls to perform end-to-end tests. The intra-service communications and data transmissions are tested.Regression Testing
This test ensures that bug fixes or new features shouldn’t break the existing behaviors of APIs.Load Testing
This tests applications’ performance by simulating different loads. Then we can calculate the capacity of the application.Stress Testing
We deliberately create high loads to the APIs and test if the APIs are able to function normally.Security Testing
This tests the APIs against all possible external threats.UI Testing
This tests the UI interactions with the APIs to make sure the data can be displayed properly.Fuzz Testing
This injects invalid or unexpected input data into the API and tries to crash the API. In this way, it identifies the API vulnerabilities.
REST API Vs. GraphQL
When it comes to API design, REST and GraphQL each have their own strengths and weaknesses.
REST
Uses standard HTTP methods like GET, POST, PUT, DELETE for CRUD operations.
Works well when you need simple, uniform interfaces between separate services/applications.
Caching strategies are straightforward to implement.
The downside is it may require multiple roundtrips to assemble related data from separate endpoints.
GraphQL
Provides a single endpoint for clients to query for precisely the data they need.
Clients specify the exact fields required in nested queries, and the server returns optimized payloads containing just those fields.
Supports Mutations for modifying data and Subscriptions for real-time notifications.
Great for aggregating data from multiple sources and works well with rapidly evolving frontend requirements.
However, it shifts complexity to the client side and can allow abusive queries if not properly safeguarded
Caching strategies can be more complicated than REST.
The best choice between REST and GraphQL depends on the specific requirements of the application and development team. GraphQL is a good fit for complex or frequently changing frontend needs, while REST suits applications where simple and consistent contracts are preferred.
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