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It basically means making sure your customers understand exactly what you can offer, through digital media such as websites, SEO and social media. Please activate some Widgets. Matching passengers and drivers while adjusting prices to balance supply and demand is a complex and hard problem. So is routing questions and answers to the people most likely to answer and read them while scaling a community and maintaining quality. Or developing a rich document editor that works well across all devices and supports real-time collaboration.
But other times, the complexity that we wrestle with is complexity that we introduced ourselves. We wrote code in a new programming language that few people knew and now we have to maintain it. Or we introduced a feature that few people use but that consumes a disproportionate amount of our time through fixes and bug reports.
Additional complexity imposes many hidden costs. They also have repercussions on how much time and effort we spend maintaining it going forward.
Too much complexity increases cognitive overhead and introduces additional friction to getting things done. It seeps through a team in a number of different ways — most directly through code, system, and product complexity but indirectly through organizational complexity. In a complicated codebase, each line of code may interact with and affect many other lines of code.
When code is too complex, it becomes harder to ramp up, harder to reason about it, harder to fix bugs. Or they may avoid working in those areas all together, even if the work can be high-impact. When Pinterest was initially scaling their site back in to handle rapid growth, they used 6 different storage technologies MySQL, Cassandra, Membase, Memcache, Redis, MongoDB across a backend team of only 3 engineers.
Each new technology promised on paper to address some limitation of their existing system, but in reality, each new solution just failed in its own special way and took more time and effort to manage and maintain. Eventually, the team learned that it would be simpler to scale by adding more machines rather than more technologies, so they eliminated systems like Cassandra and MongoDB and strengthened the remaining components of their architecture.
Fragmenting infrastructure into too many systems brings many hidden costs. Attention gets splintered across the multiple systems. It becomes harder to pool together resources to build reusable libraries for each system, harder to ramp up new people for pager duty, harder to understand the particular failure modes and performance characteristics of each system.
When tools and abstractions are too complex or there are too many of them, they become difficult for the team to understand and discover. Product complexity can result from an ill-defined vision or an untempered ambition leading to a lack of product focus. This desire to be great at many things instead of just one core area sometimes manifests itself in an inability to concisely explain the purpose of a product to new users.
Product complexity leads to more code and system complexity — teams add more code and more infrastructure to support new features. When a product has a wide surface area, adding a new feature or modifying an existing one requires expending a large amount of effort to understand and accommodate the olds ones. An overly complex product means that there are more code branches, more issues to think through, more bug reports that the team needs to address. Engineers and data scientists need to analyze more variables and do more one-off reports, rather than focusing on understanding core user behavior.
Engineers need to invest more time to ramp up on the feature space and be productive.
Everyone ends up context switching between more projects. And the time spent maintaining all these features is time not spent re-investing in the code, repaying technical debt, or strengthening abstractions.
Code, system, and product complexity in turn breed organizational complexity. Larger teams mean more communication overhead, more coordination, and lower overall efficiency. And, of course, all the new hires have to be trained and onboarded.
French This page contains the stable versions of the French language packs compatible with the stable versions of the phpBB forum software to fully localise the board interface into French. Select the Web server that should be automatically configured to run phpMyAdmin. Product complexity leads to more code and system complexity — teams add more code and more infrastructure to support new features. All of these activities help provide checks and balances that individual engineers may other introduce. Remember when decompressing the packages that you must retain the directory structure.
The alternative to hiring more people is splitting the engineering organization into smaller teams — perhaps even creating one-person teams — to cover the large code, system, and product surface area. This reduces the communication overhead, but one-person teams have their own costs.
There are fewer opportunities to work with other people, which can hurt workplace happiness and employee retention. Unless everyone is conscious and proactive about asking for feedback, individuals might receive less feedback about their work because there are fewer people who share the same project context. The reduced feedback can lead to lower quality code or inadvertent complexity being introduced into the codebase or infrastructure.
One way is to make it so simple that there are obviously no deficiencies and the other way is to make it so complicated that there are no obvious deficiencies. Resist the urge to add more complexity. Reason through the maintenance costs. Define a mission statement for your team or product to align focus.
In Team Geek, Brian W. Compose large systems from simpler building blocks.
Google is an example of an organization that focuses on building strong, core abstractions that then become widely adopted for a wide variety of applications. They have basic building blocks like Protocol Buffers, the Google File System, and Stubby servers for remote procedure calls. And on top of those, thousands of applications including large-scale web indexing, Google Analytics site-tracking, Google News clustering, Google Earth data processing, Google Zeitgeist data analysis, and many more are then built. Clearly define interfaces between modules and services.