Software Engineering Daily

Physical places have a large amount of latent data. Pick any location on a map, and think about all of the questions you could ask about that location. What businesses are at that location? How many cars pass through it? What is the soil composition? How much is the land on that location worth? The world of web-based information has become easy to query. We can use search engines like Google, as w...

Descript is a software product for editing podcasts and video. Descript is a deceptively powerful tool, and its software architecture includes novel usage of transcription APIs, text-to-speech, speech-to-text, and other domain-specific machine learning applications. Some of the most popular podcasts and YouTube channels use Descript as their editing tool because it provides a set of features that...

Lyft is a ridesharing company that generates a high volume of data every day.  This data includes ride history, pricing information, mapping, routing, and financial transactions. The data is stored across a variety of different databases, data lakes, and queueing systems, and is processed at scale in order to generate machine learning models, reports, and data applications. Data workflows involve...

Cloud computing caused a fundamental economic shift in how software is built. Before the cloud, businesses needed to buy physical servers in order to operate. There was an up-front cost that often amounted to tens of thousands of dollars required to pay for these servers.  Cloud computing changed the up-front capital expense to an ongoing operational expense, with businesses increasingly shifting...

GraphQL is a system that allows frontend engineers to make requests across multiple data sources using a simple query format. In GraphQL, a frontend developer does not have to worry about the request logic for individual backend services. The frontend developer only needs to know how to issue GraphQL requests from the client, and these requests are handled by a GraphQL server. GraphQL is mostly us...

A data warehouse serves the purpose of providing low latency queries for high volumes of data. A data warehouse is often part of a data pipeline, which moves data through different areas of infrastructure in order to build applications such as machine learning models, dashboards, and reports. Modern data pipelines are often associated with the term “ELT” or Extract, Load, Transform. In the “ELT” w...

ReactJS developers have lots of options for building their applications, and those options are not easy to work through. State management, concurrency, networking, and testing all have elements of complexity and a wide range of available tools. Take a look at any specific area of JavaScript application development, and you can find highly varied opinions. Kent Dodds is a JavaScript teacher who foc...

JavaScript fatigue. This phrase has been used to describe the confusion and exhaustion around the volume of different tools required to be productive as a JavaScript developer. Frameworks, package managers, typing systems, state management, GraphQL, and deployment systems–there are so many decisions to make. In addition to the present-day tooling choices, a JavaScript developer needs to watch the...

Full-stack JavaScript applications have been possible since the creation of NodeJS in 2009. Since then, the best practices for building and deploying these applications have steadily evolved with the technology.  ReactJS created consolidation around the view layer. The emergence of AWS Lambda created a new paradigm for backend execution. Serverless tools such as DynamoDB offer autoscaling abstract...

ReactJS began to standardize frontend web development around 2015. The core ideas around one-way data binding, JSX, and components caused many developers to embrace React with open arms. There has been a large number of educators that have emerged to help train developers wanting to learn React. A new developer learning React has numerous questions around frameworks, state management, rendering, a...

When ReactJS became popular, frontend web development became easier. But React is just a view layer. Developers who came to React expecting a full web development framework like Ruby on Rails or Django were required to put together a set of tools to satisfy that purpose. A full-stack JavaScript framework has numerous requirements. How does it scale? How does it handle server-side rendering versus...

Low code tools can be used to build an increasing number of applications. Knowledge workers within a large corporation can use low code tools to augment their usage of spreadsheets. Entrepreneurs can use low code tools to start businesses even without knowing how to code. Modern low code tools have benefited from steady improvements in cloud infrastructure, front-end frameworks like ReactJS, and b...

Slack is a messaging application with millions of users. The desktop application is an Electron app, which is effectively a web browser dedicated to running Slack. This frontend is built with ReactJS and other JavaScript code, and the application is incredibly smooth and reliable, despite its complexity. When a user boots up Slack, the application needs to figure out what data to fetch and where t...

Every company has a large number of routine data workflows. These data workflows involve spreadsheets, CSV files, and tedious manual work to be done by a knowledge worker.  For example, data might need to be taken from Salesforce, filtered for new customers, and piped into Mailchimp. Or perhaps you need to sort all your customers to find only the ones who have spent more than $50. These data workf...

Cryptocurrencies today serve two purposes: store of value and speculation.  The application infrastructure that has been built around cryptocurrency is mostly to support these use cases. At some point in the future, perhaps cryptocurrencies can be used as a global medium of exchange that is accepted at the grocery store. Perhaps we will use the blockchain for supply chain management, and as a univ...

At Airbnb, infrastructure management is standardized across the organization. Platform engineering teams build tools that allow the other teams throughout the organization to work more effectively. A platform engineering team handles problems such as continuous integration, observability, and service discovery. Other teams throughout a company use the tools that a platform engineering team builds....

A gig economy application generates lots of notifications.  There is SMS, mobile phone updates, emails, and native application updates. If you order a ride from Uber, you might receive a text message and a push notification at the same time. If an app overloads the user with notifications, the user might end up annoyed and delete the app from their phone.  But perhaps all of these notifications ar...

In a modern data platform, distributed streaming systems are used to read data coming off of an application in real-time. There are a wide variety of streaming systems, including Kafka Streams, Apache Samza, Apache Flink, Spark Streaming, and more.  When Eric Anderson joined the show back in 2016, he was working at Google on Google Cloud Dataflow, a managed service for handling streaming data. Tod...

Distributed stream processing frameworks are used to rapidly ingest and aggregate large volumes of incoming data. These frameworks often require the application developer to write imperative logic describing how that data should be processed.  For example, a high volume of clickstream data that is getting buffered to Kafka needs to have a stream processing system evaluate that data to prepare it f...

A cloud provider gives developers access to virtualized server infrastructure. When a developer rents this infrastructure via an API call, a virtual server is instantiated on physical machines. That virtual server needs to be made addressable through the allocation of an IP address to make it reachable from the open Internet. When the virtual server starts to receive too much traffic, that traffic...

A data pipeline is a series of steps that takes large data sets and creates usable results from them. At the beginning of a data pipeline, a data set might be pulled from a database, a distributed file system, or a Kafka topic. Throughout a data pipeline, different data sets are joined, filtered, and statistically analyzed. At the end of a data pipeline, data might be put into a data warehouse or...

A data warehouse provides low latency access to large volumes of data.  A data warehouse is a crucial piece of infrastructure for a large company, because it can be used to answer complex questions involving a large number of data points. But a data warehouse usually cannot hold all of a company’s data at any given time. Users need to move a subset of the data into the data warehouse by reading la...

Machine learning applications are widely deployed across the software industry.  Most of these applications used supervised learning, a process in which labeled data sets are used to find correlations between the labels and the trends in that underlying data. But supervised learning is only one application of machine learning. Another broad set of machine learning methods is described by the term...

Distributed stream processing systems are used to read large volumes of data and perform operations across those data streams.  These stream processing systems often build off of the MapReduce algorithm for collecting and aggregating large volumes of data, but instead of processing a calculation over a single large batch of data, they process data on an ongoing basis. There are so many different s...

Large companies generate large volumes of data. This data gets dumped into a data lake for long-term storage, then pulled into memory for processing and analysis. Once it is in memory, it is often read into a dashboard, which presents a human with a visualization of the data.  The end-user who is consuming this data is often a data scientist who is looking at the data to find trends and design new...