Association of Investment Marketers

Simple, reliable data integration for analytics teams
Focus on analytics, not engineering. Our prebuilt connectors deliver analysis-ready schemas and adapt to source changes automatically.

Leave the engineering to us
Fivetran automates data integration from source to destination, providing data your team can analyze immediately.

Fivetran Schematic

I got what I wanted, something automated and trustworthy. Just click a few buttons, enter your credentials, and it’s all up and running. Fivetran does its job.

Edward Mancey

Head of BI, Brandwatch

There were three main reasons we needed to bring on Fivetran: easier data integration, quicker access, and the ability to construct more complex data models.

Kade Killary

Data Scientist, Crossmedia

We really like what Fivetran enables us to do. It is incredibly easy, fast and reliable. This has been a paradigm shift for us — it is the future of data pipelines and ETL and Fivetran is at the forefront of it.

Marcus Laanen

Senior Manager BI, DocuSign

Customer Testimonial Videos

Case Studies & Insight


Data Warehouse Benchmark 2020

Over the last two years, the major cloud data warehouses have been in a near-tie for performance. Redshift and BigQuery have both evolved their user experience to be more similar to Snowflake. The market is converging around two key principles: separation of compute and storage, and flat-rate pricing that can “spike” to handle intermittent workloads.Fivetran is a data pipeline that syncs data from apps, databases and file stores into our customers’ data warehouses. The question we get asked most often is, “What data warehouse should I choose?” In order to better answer this question, we’ve performed a benchmark comparing the speed and cost of four of the most popular data warehouses:

  • Amazon Redshift
  • Snowflake
  • Presto
  • Google BigQuery

Benchmarks are all about making choices: What kind of data will I use? How much? What kind of queries? How you make these choices matters a lot: Change the shape of your data or the structure of your queries and the fastest warehouse can become the slowest. We’ve tried to make these choices in a way that represents a typical Fivetran user, so that the results will be useful to the kind of company that uses Fivetran.

A typical Fivetran user might sync Salesforce, JIRA, Marketo, Adwords and their production Oracle database into a data warehouse. These data sources aren’t that large: A typical source will contain tens to hundreds of gigabytes. They are complex: They contain hundreds of tables in a normalized schema, and our customers write complex SQL queries to summarize this data.

Download the full report here (external link).

Fivetran Tech Image

Why You Should Be Analyzing Your Marketing Stack

As an enterprise marketer, you have outreach and lead generation efforts spread across dozens if not hundreds of channels. From paid advertising to customer relationship management to marketing automation and beyond, your tool set or marketing stack helps you conduct, measure and continuously improve your efforts.

However, if you’re not harvesting and analyzing the data from these disparate systems, how do you know, truly, which marketing avenues and campaigns are winning?

Marketing’s Changing Times
Marketing has evolved greatly in the last ten years. Job titles such as Marketing Director, Marketing Manager, Growth Manager and so on are expected to intelligently report success metrics of all their efforts. This means marketers need to know how to infer data trends, adapt to changing marketplaces, and perhaps know a little SQL or be an Excel ninja to adequately report back to the rest of the business. In many enterprise companies, marketing is a leading data-driven department.

The Data Challenges of a Marketing Stack
Components of a marketing stack often include built-in reporting. Tools such as Salesforce, MailChimp, Google Search Console, Clearbit and others have business intelligence capabilities including dashboards and trendlines. These reporting tools are a good starting point, but today’s savvy businesses analyze data across tools and mediums to report back on questions such as:

  • Which social ads translate to the highest sales conversions on our Shopify site?
  • How many touches on content does it take to guide someone down our sales funnel?
  • Which event types are providing our sales team with the most qualified leads?

Continue here …


The Modern Data Science Stack

It’s an oft-cited factoid that data scientists spend only 20% of their time doing actual data science work, while the rest of their time is spent on doing what is often delightfully referred to as “data munging” — that is, obtaining, cleaning, and preparing data for analysis. Regardless of how true that statistic actually is, most data scientists I know agree with the sentiment — they spend a lot of their time on tasks that do not actually require much science.

Grossly generalizing, the traditional data science workflow involves a lot of custom data extraction and cleaning work. A data scientist comes up with an idea, goes on the hunt for data, talks with a few other people about where those data might be stored and what their quirks might be, iterates a few times “cleaning” the data and discovering heretofore unknown quirks, and only after all of that process are they able to actually start working on their statistical or machine learning (ML) model*. Then, once they have an ML model that works sufficiently well, they have to try to get that model somewhere so that the predictions it produces can actually be used. In this process, the ML component, the actual “data science” component, is the least time-consuming of the whole process!

And this is a bummer! If you’re a data scientist, you want to spend your time sciencing the data, not doing tedious data-cleaning work. And if you’re a person who hires and employs data scientists, you want them spending their time on the high-value work that only data scientists can do, not re-writing the same core business definitions over and over.

In this blog post, I’m going to talk about how we as data scientists can make use of a data infrastructure paradigm semi-commonly known as The Modern Data Stack to work more efficiently and ship more ML models, faster. By building our data infrastructure on top of the modern data stack, we can make our data science more efficient, our machine-learning more robust, and our research cycle more rapid.

Continue here …

Prebuilt connectors

Centralize your operational data in minutes with 150+ zero-configuration connectors.

Ready-to-query schemas

Use thoughtful, research-driven schemas and ERDs for all your sources.

Automated schema migrations

Save resources with connectors that automatically adapt to schema and API changes.

Fully managed data integration

Reduce technical debt with scalable connectors managed from source to destination.

SQL-based transformations

Model your business logic in any destination using SQL, the industry standard.

Incremental batch updates

Change data capture delivers incremental updates for all your sources.

Ciara Roca

Ciara Roca

Marketing Director, EMEA

I’m passionate about the value of digital technology to enhance all aspects of business performance for our customers, from AI accounting technology to cloud-based business management.




Submit a Comment