Software Engineering Daily

Relational databases have been popular since the 1970s, but in the last 20 years the amount of data that applications need to collect and store has skyrocketed. The raw cost to store that data has decreased. There is a common phrase in software companies: “it costs you less to save the data than to throw

Ten years ago, a biology researcher was limited by the software tools available. Most of the electronic record keeping was done using Excel and other general purpose tools. Benchling is a suite of software tools that were designed to simplify the lives of life science researchers. Benchling helps with sample tracking, experiment design, and workflow

When a user takes a ride on Uber, the app on the user’s phone is communicating with Uber’s backend infrastructure, which is writing to a database that maintains the state of that user’s activity. This database is known as a transactional database or “OLTP” (online transaction processing). Every active user and driver and UberEATS restaurant

Cloud computing lowered the cost and improved accessibility to tools for storing large volumes of data. In the early 2000s, Hadoop caused a revolution in large scale batch processing. Since then, companies have been building ways to store and access their data faster and more efficiently. At the same time, the sheer volume of data

Phones are constantly tracking the location of a user in space. Devices like cars, smart watches, and drones are also picking up high volumes of location data. This location data is also called “geospatial data.” The amount of geospatial data is rapidly increasing, and there is a growing demand for software to perform operations over

There are hundreds of different databases. There are tens of continuous delivery products. There is an ocean of cloud providers and CRM systems and monitoring platforms and sales prospecting tools. The range of available software products is so diverse that it can be overwhelming to figure out which products to buy. Siftery is a company

Machine learning tools are rapidly maturing. TensorFlow gave developers an open source version of Google’s internal machine learning framework. Cloud computing provides a cost effective, accessible way of training models. Edge computing allows for low latency deployments of models. But even if you are a kid with a laptop who has learned all the machine

Data streams about the weather can be used to predict how soybean futures are going to change in price. Satellite data streams can take pictures of the number of cars on the road, and judge how traffic patterns are changing. Search engines can aggregate data from different queries and determine what people are most interested

Farms have lots of data. A corn farmer needs to monitor the chemical composition of soil. A soybean farmer needs to track crop yield. A chicken farmer needs to count the number of eggs produced. If this data is captured, it can be acted upon—for example, a dry farm can automatically turn up its irrigation

Apache Spark is a system for processing large data sets in parallel. The core abstraction of Spark is the resilient distributed dataset (RDD), a working set of data that sits in memory for fast, iterative processing. Matei Zaharia created Spark with two goals: to provide a composable, high-level set of APIs for performing distributed processing;