Web and mobile analytics are an important part of any business, and difficult to get right. The most frustrating part is when you realize that you haven’t been tracking a key interaction, having to write custom logic to add that event, and then waiting to collect data. Heap is a platform that automatically tracks every event so that you can retroactively decide which actions are important to your business and easily build reports with or without SQL. In this episode Dan Robinson, CTO of Heap, describes how they have architected their data infrastructure, how they build their tracking agents, and the data virtualization layer that enables users to define their own labels.
With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages. To address these shortcomings the engineers at Cockroach Labs have built a globally distributed SQL database with full ACID semantics in Cockroach DB. In this episode Peter Mattis, the co-founder and VP of Engineering at Cockroach Labs, describes the architecture that underlies the database, the challenges they have faced along the way, and the ways that you can use it in your own environments today.
Using a multi-model database in your applications can greatly reduce the amount of infrastructure and complexity required. ArangoDB is a storage engine that supports documents, dey/value, and graph data formats, as well as being fast and scalable. In this episode Jan Steeman and Jan Stücke explain where Arango fits in the crowded database market, how it works under the hood, and how you can start working with it today.
Building an ETL pipeline is a common need across businesses and industries. It’s easy to get one started but difficult to manage as new requirements are added and greater scalability becomes necessary. Rather than duplicating the efforts of other engineers it might be best to use a hosted service to handle the plumbing so that you can focus on the parts that actually matter for your business. In this episode CTO and co-founder of Alooma, Yair Weinberger, explains how the platform addresses the common needs of data collection, manipulation, and storage while allowing for flexible processing. He describes the motivation for starting the company, how their infrastructure is architected, and the challenges of supporting multi-tenancy and a wide variety of integrations.
Most businesses end up with data in a myriad of places with varying levels of structure. This makes it difficult to gain insights from across departments, projects, or people. Presto is a distributed SQL engine that allows you to tie all of your information together without having to first aggregate it all into a data warehouse. Kamil Bajda-Pawlikowski co-founded Starburst Data to provide support and tooling for Presto, as well as contributing advanced features back to the project. In this episode he describes how Presto is architected, how you can use it for your analytics, and the work that he is doing at Starburst Data.
The Open Data Science Conference brings together a variety of data professionals each year in Boston. This week’s episode consists of a pair of brief interviews conducted on-site at the conference. First up you’ll hear from Andy Eschbacher of Carto. He dscribes some of the complexities inherent to working with geospatial data, how they are handling it, and some of the interesting use cases that they enable for their customers. Next is Todd Blaschka, COO of TigerGraph. He explains how graph databases differ from relational engines, where graph algorithms are useful, and how TigerGraph is built to alow for fast and scalable operation.
The Open Data Science Conference brings together a variety of data professionals each year in Boston. This week’s episode consists of a pair of brief interviews conducted on-site at the conference. First up you’ll hear from Alan Anders, the CTO of Applecart about their challenges with getting Spark to scale for constructing an entity graph from multiple data sources. Next I spoke with Stepan Pushkarev, the CEO, CTO, and Co-Founder of Hydrosphere.io about the challenges of running machine learning models in production and how his team tracks key metrics and samples production data to re-train and re-deploy those models for better accuracy and more robust operation.
Business Intelligence software is often cumbersome and requires specialized knowledge of the tools and data to be able to ask and answer questions about the state of the organization. Metabase is a tool built with the goal of making the act of discovering information and asking questions of an organizations data easy and self-service for non-technical users. In this episode the CEO of Metabase, Sameer Al-Sakran, discusses how and why the project got started, the ways that it can be used to build and share useful reports, some of the useful features planned for future releases, and how to get it set up to start using it in your environment.
The information about how data is acquired and processed is often as important as the data itself. For this reason metadata management systems are built to track the journey of your business data to aid in analysis, presentation, and compliance. These systems are frequently cumbersome and difficult to maintain, so Octopai was founded to alleviate that burden. In this episode Amnon Drori, CEO and co-founder of Octopai, discusses the business problems he witnessed that led him to starting the company, how their systems are able to provide valuable tools and insights, and the direction that their product will be taking in the future.
The rate of change in the data engineering industry is alternately exciting and exhausting. Joe Crobak found his way into the work of data management by accident as so many of us do. After being engrossed with researching the details of distributed systems and big data management for his work he began sharing his findings with friends. This led to his creation of the Hadoop Weekly newsletter, which he recently rebranded as the Data Engineering Weekly newsletter. In this episode he discusses his experiences working as a data engineer in industry and at the USDS, his motivations and methods for creating a newsleteter, and the insights that he has gleaned from it.