WHY IT MATTERS: Digital Transformation

APM continues to be critical for modern, multicloud observability. Read the report where Gartner defines the market as "software that enables the observation of application behaviour and its infrastructure dependencies, users, and business key performance indicators (KPIs) throughout the application’s life cycle," and see why we’re positioned highest and furthest in both Completeness of Vision & Ability to Execute.

Hello, 2022! Following my wrap-up of martech from 2021 — tl;dr a year in digital marketing that was experienced like a Twitch stream at 1.5X speed — let's jump in to the martech topics I expect will shape 2022.

To succeed with data and analytics initiatives, enterprises need a comprehensive view of critical technology capabilities. We believe these Hype Cycles draw a holistic view of technologies — including the new addition of lakehouse architecture as a trigger, which can make data and analytics programs modern, broadly applied, richer in functionality and successful.

Get the report and gain insight into:
-Changes in this Hype Cycle — including the addition of Lakehouse
-The current state of data management maturity
-The Priority Matrix

Klipfolio connects with virtually any data source behind your business, giving you real-time visibility into your core performance, growth, and efficiency metrics that signal your health and success. Get started now.

Josh Evans talks about the chaotic and vibrant world of microservices at Netflix. He starts with the basics- the anatomy of a microservice, the challenges around distributed systems, and the benefits. Then he builds on that foundation exploring the cultural, architectural, and operational methods that lead to microservice mastery.

Learn all the most important ecommerce metrics you should start tracking in your reporting dashboard!

2021 was the year of broken supply chains, Amazon aggregators, more advertising, Shopify's almost-marketplace, and one unanswered question - did the pandemic boost e-commerce after all? For now, the world of marketplaces revolves around Amazon - it has doubled in size in two years. No one will challenge it directly, but a different paradigm might.

Successful companies are meeting this challenge by taking a “customer-back” perspective. Based on a clear understanding of the skills, behaviors, and mindsets the organization requires, these companies identify capability gaps and improvement opportunities across their workforce. Then, instead of offering “one size fits all” training, they design their capability programs based on detailed analysis of the needs of individual learners—while instilling a culture that fosters learning throughout the organization. That involves the application of a few powerful principles.

In Santiment’s end-of-year digest, we look back at some of 2021’s most important market trends and what they tell us about the year ahead - all from the prism of on-chain, social and development activity.

“Self-hosting” is a practice that pretty much describes itself: running your own internet services, typically on hardware you own and have at home. This contrasts with relying on products from large tech companies, which the user has no direct involvement in. A self-hoster controls it all, from the hardware used to the configuration of the software.

Visual editors for HTML, CSS, and SVG: Customize, curate, and publish designs faster than ever.

In this article, we’ll present seven of the key best practices you should adhere to when designing, implementing and operationalizing your data lake. These are things that will mostly be automatically implemented if you’re using Upsolver, but if you’re building your data lake ‘manually’ you’ll need to ensure they’re done correctly if you want to get the most value out of your data lake architecture.
While we will mostly be referring to Amazon S3 as the storage layer, these best practices would also apply for a data lake built on a different cloud or using Hadoop on-premise.

Data lakes are key to scalable, open data architectures – but they can pose challenges to data engineering teams. Efficient data integration is the difference between a bunch of log files sitting in Amazon S3 or Azure Storage and a high-performance data lake that provides real value to analytics, data science and engineering teams.
In this guide we will cover some of the essential best practices and guidelines on how to make your data lake analytics-ready using cloud data processing and integration tools.

Here’s a sample communication architecture for a remote-friendly company.

While CDP vendors may claim to do everything, the fact is they can’t.

We need to be able to fit new concepts and components neatly into existing architecture—to adapt to changes without disruption.

At first glance, the data fabric and the data mesh concepts sound quite similar. Meshes are often made from a type of fabric, after all, and they are both malleable items that can be lain atop things–in this case, your IT systems that are subject to the ever-growing data crush.

But there are fundamental differences to these two approaches, so it’s worth taking some time to learn their differences.

There are many tools available for doing large-scale data analysis, and picking the right one for a given job is critical. Here, we take an in-depth analysis of the architecture and performance characteristics of a completely serverless data processing platform. While the approach isn’t applicable for every use case, it has a very low total cost of ownership (TCO) and because AWS Lambda allows us to run arbitrary code easily, this approach provides the flexibility to handle non-standard data formats easily.

Where should I run my stuff on Google Cloud? #GCPSketchnote

Emerging Technology Roadmap benchmark adoption plan, risk factors, and value drivers. Use this roadmap to help you make technology investment decisions for the coming 12- to 24-month time period.

Performance chapter of the 2021 Web Almanac covering Core Web Vitals (Largest Contentful Paint, Cumulative Layout Shift, First Input Delay) as well as First Contentful Paint and Time to First Byte

The goal of the Web Almanac’s Structured Data chapter is to explore how structured data is currently being used across the web. We hope that this will provide insight into the landscape, the challenges, and the opportunities at hand.

Fluency on the command line is a skill often neglected or considered arcane, but it improves your flexibility and productivity as an engineer in both obvious and subtle ways. This is a selection of notes and tips on using the command-line that we've found useful when working on Linux. Some tips are elementary, and some are fairly specific, sophisticated, or obscure. This page is not long, but if you can use and recall all the items here, you know a lot.

Within a matter of hours of each day beginning, we ingest tens of billions of records from online sources to HDFS, aggregated across 50+ offline data flows, and visualize 40+ metrics across our business lines to support a dashboard providing company leaders with timely and critical business insights. Dubbed the Daily Executive Dashboard (DED), this dashboard contains critical growth, engagement, and bookings metrics that indicate the health of LinkedIn’s business. In this blog post, we’ll spotlight the challenges to DED’s delivery and performance posed by the growing complexity of computing and aggregating these metrics, and how this dashboard transformed our approach to data pipelines across the company.

This year's State of the API report covers the largest and most comprehensive survey on APIs, ever. More than 28,000 developers and API professionals shared their thoughts on a range of topics, including their organizations' development priorities, how they get their work done, and where they see the industry going. We combined this data with data we've observed on the Postman API Platform to build a robust picture of the current state—and the future—of APIs.