Content Marketing for Data Services

The model of a modern marketing machine

 

We Speak Data

Need a writer who understands the nuances between data lakes and data warehouses? Edify can get you up and running — no Data Science 101 needed.

Market Experience

Edify has experience being the buyer for data services solutions. We have deep understanding of your buyers and their needs, questions, and pains. We bring all that insight to your content strategy.

Our work in data services

  • Thought leadership article

    How to Use Metadata to Future Proof Your Data Stack

    “We believe that making the metadata drive the stack provides clean separation of concerns and encourages simplicity. Plus, it helps data teams think strategically and use the right tools to fulfill their data strategy needs.”

  • Educational blog post

    Data Warehouses, Data Lakes, and Data Lakehouses– What about Data Warelakes?

    “For companies that have semi-structured or unstructured data and they want to do some sophisticated data processing, which is not representable in SQL, then a data lake gives them the flexibility they need to incorporate multiple kinds of computation on the same data.”

  • eWeek article

    How to Speed Up Your Software Testing Cycle: 5 Key Tips

    “It’s considered best practice to keep code changes small in scale, reducing the scope and breadth of change. Most continuous integration automation, however, kicks off comprehensive testing suites that test thousands or millions of lines of code, even when few lines of code changed.”

  • Brand positioning blog

    Third-Party Tracking Is Dead

    “Residential demographic data allows us to see that a man named Marshall has moved from a one-bedroom home to a two-bedroom home. From that, we can deduce with very high accuracy that Marshall got married.

    We turned this logic into a feature engineering tool, building many of these ground truth insights on top of the initial data.”

  • Case study

    Datacoral’s Pipelines Translate Greenhouse Data Into an Easily Analyzable Structure

    “By using Datacoral’s CDC connector, Greenhouse was able to build a model using real production data. Datacoral’s orchestrated transformation capabilities implement a simple machine learning pipeline that suggests a person to facilitate a given interview. Since interviews can be facilitated by one or multiple individuals, Greenhouse built a rules-based recommendation algorithm that provided the most likely individual name a recruiter would pick.”

  • Test acceleration article

    I Like to Merge It, Merge It: Why You Should Merge Frequently

    “In today’s agile-dominated software world, new releases are targeted for production every sprint. (It’s worth noting that some companies release several times a day!) Frequent releases are made possible by small, well-defined commits that are easier to merge into a mainline branch. Such changes are also easier to build and test.”