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Michelle Obama and Politics: A (Sort of) Love Story
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A system built on events no longer passively stores a dataset and waits to receive commands from a UI-driven application. Instead, it is designed to support the flow of data throughout a business and the real-time reaction and processing that happens in response to each event that occurs in the business.

The founders of Confluent originally created the open source project Apache Kafka while working at LinkedIn, and over recent years Kafka has become a foundational technology in the movement to event streaming. It was clear as we dove into this challenge that there was no off-the-shelf solution to this problem.

With stream processing, the ETA becomes a continuous calculation always in sync with the position of the car. In a social network, an event could represent a click, an email, a login, a new connection, or a profile update. Treating this data as an ever-occurring stream made it accessible to all the other systems LinkedIn had.

Michelle Obama - Becoming - Book Talk - Dec 11 2018

Over time, the use of Kafka spread to security systems, low-level application monitoring, email, newsfeeds, and hundreds of other applications. This all happened in a context that required massive scale, with trillions of messages flowing through Kafka each day, and thousands of engineers building around it. After we released Kafka as open source, it started to spread well outside LinkedIn, with similar architectures showing up at Netflix, Uber, Pinterest, Twitter, Airbnb, and others.

As we left LinkedIn to establish Confluent in , Kafka and event streams had begun to garner interest well beyond the Silicon Valley tech companies, and moved from simple data pipelines to directly powering real-time applications. Some of the largest banks in the world now use Kafka and Confluent for fraud detection, payment systems, risk systems, and microservices architectures.

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In retail, companies like Walmart , Target , and Nordstrom have adopted Kafka. Projects include real-time inventory management, in addition to integration of ecommerce and brick-and-mortar facilities. Retail had traditionally been built around slow, daily batch processing, but competition from ecommerce has created a push to become integrated and real time.

A number of car companies, including Tesla and Audi , have built out the IoT platform for their next-generation connected cars, modeling car data as real-time streams of events. Trains, boats, warehouses, and heavy machinery are starting to be captured in event streams as well. In most of these companies, Kafka was initially adopted to enable a single, scalable, real-time application or data pipeline for one particular use case.

This initial usage tends to spread rapidly within a company to other applications. To take retail as an example, a retailer might begin by capturing the stream of sales that occur in stores for a single use case, say, speeding up inventory management. Each event might represent the data associated with one sale: which products sold, what store they sold in, etc. But though usage might start with a single application, this same stream of sales is critical for systems that do pricing, reporting, discounting, and dozens of other use cases.

Indeed, in a global retail business there are hundreds or even thousands of software systems that manage the core processes of the business from inventory management, warehouse operations, shipments, price changes, analytics, and purchasing.

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How many of these core processes are impacted by the simple event of a sale of a product taking place? The answer is many or most of them, as selling a product is one of the most fundamental activities in retail. This is a virtuous cycle of adoption. The first application brings with it critical data streams. New applications join the platform to get access to those data streams, and bring with them their own streams. Streams bring applications, which in turn bring more streams.

The core idea is that an event stream can be treated as a record of what has happened, and any system or application can tap into this in real time to react, respond, or process the data stream. This has very significant implications. Internally, companies are often a spaghetti-mess of interconnected systems, with each application jury-rigged to every other.

This is an incredibly costly, slow approach. Event streams offer an alternative: there can be a central platform supporting real-time processing, querying, and computation. Each application publishes the streams related to its part of the business and relies on other streams, in a fully decoupled manner. In driving interconnection, the event streaming platform acts as the central nervous system for the emerging software-defined company.

We can think of the individual, disconnected UI-centric applications as a kind of single-celled organism of the software world. A multicell animal has a nervous system that coordinates all the individual cells into a single entity that can respond, plan, and act instantaneously to whatever it experiences in any of its constituent parts. A digital company needs a software equivalent to this nervous system that connects all its systems, applications, and processes. This is what makes us believe this emerging event streaming platform will be the single most strategic data platform in a modern company.

Becoming by Michelle Obama review – race, marriage and the ugly side of politics

That is insufficient for the current state, let alone the emerging trends. What is needed is a real-time data platform that incorporates the full storage and query processing capabilities of a database into a modern, horizontally scalable, data platform. And the needs for this platform are more than just simply reading and writing to these streams of events. An event stream is not just a transient, lossy spray of data about the things happening now—it is the full, reliable, persistent record of what has happened in the business going from past to present.

Combining the storage and processing capabilities of a database with real-time data might seem a bit odd. If we think of a database as a kind of container that holds a pile of passive data, then event streams might seem quite far from the domain of databases. But the idea of stream processing turns this on its head.

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This leads to a fundamentally different framing of what a database can be. In a traditional database, the data sits passively and waits for an application or person to issue queries that are responded to. In stream processing, this is inverted: the data is a continuous, active stream of events, fed to passive queries that simply react and process that stream. In some ways databases already exhibited this duality of tables and streams of events in their internal design if not their external features.

Most databases are built around a commit log that acts as a persistent stream of the data modification events. This log is usually nothing more than an implementation detail in traditional databases, not accessible to queries.

Becoming by Michelle Obama – review | Books | The Guardian

However in the event streaming world the log needs to become a first-class citizen along with the tables it populates. The case for integrating these two things is based on more than database internals, though.

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Applications are fundamentally about reacting to events that occur in the world using data stored in tables. In our retail example, a sale event impacts the inventory on hand state in a database table , which impacts the need to reorder another event! Whole business processes can form from these daisy chains of application and database interactions, creating new events while also changing the state of the world at the same time reducing stock counts, updating balances, etc.

Barack suffers no such anxiety: since he is unique, what group or class could he possibly belong to? Their relationship develops as a disputatious comedy of manners. On their first date, he wears a white linen blazer that resembles a cast-off from Miami Vice. He also smokes, which disgusts her. During their courtship, Barack writes deeply pondered letters while Michelle insists on spontaneous phone calls.

Treble and bass clash again and are gently reconciled when Michelle describes the way power institutionalised the two of them as P otus and Flotus or, to use their Secret Service code names, Renegade and Renaissance. The antidote to this onerous symbolism is her irrepressible lightness of being: her dance moves, her rapping with Jay Phar oah , her karaoke session with James Corden, and the happy informality that made her ignore protocol and stoop from her great height to give the Queen a consoling hug when they first met.

She is even more amused by one of their presidential pets , a dog that refused to be house-trained: why bother, since the executive mansion contained rooms? My favourite scene is a recent one, with Michelle in her new Washington home, alone one evening except for the armed guards in the garage. The character of black youth and the fate of American democracy can wait.