September 17, 2019ETL vs ELT: What’s the Difference? & How to Choose
If you’ve been following the AtScale blog throughout the years, we hope that we have provided you with content that has furthered your understanding of the big data analytics landscape and who we are as a company. Of these articles, we’ve explored concepts and best practices for data technology, responded to pressing industry news, and took you on the road with us at major events. As we’re wrapping up 2019, we compiled a list of our blog posts that you enjoyed reading the most. Here is our year in review, according to our top performing blog posts.
1. What is a Semantic Layer? Why Would You Want One?
“Businesses will have to define a semantic layer, no matter what. If you don’t have experts do it, all your end users will do it for themselves in Tableau, Qlik, Excel or whichever front end they are using.” In this post, Matt Baird, co-founder and CTO of AtScale, explains the need for the modern organization to have a semantic layer, defining what “semantic” means in the context of data warehouses. Baird takes us back to the origins of semantic layers and how it has evolved since its introduction, shares its pitfalls, and brings us up to speed on the need for it today.
2. The 6 Principles of a Modern Data Architecture
What are customers looking for when considering the move to a modern data architecture? In this post, Joshua Klahr, vice president of product management, shares his findings from conversations with customers and prospects, creating a roadmap to navigate and make sense of the world of modern data.
3. Which Cloud Data Warehouse Should I Migrate To: RedShift, BigQuery, or Snowflake?
What’s the best cloud data warehouse technology for handling analytics workloads? In a market with multiple technologies decisions are difficult to make. In this blog post, we make the decision-making process easier by outlining the benefits of each solution so you can feel comfortable making the choice that is best for your team.
4. Cloud Cost Optimization: How to Reduce Cloud Data Warehouse Costs
“The trade off of outsourcing data platform support and maintenance is the cost: if you aren’t careful it is very easy to spend a lot of money very quickly.” If you have worked with cloud-based data warehouses, you are fully aware of the benefits that come with it’s power. You may also be aware of the financial strains that it may bring to your organization. In this post, we share our money-saving tips so that you can still become a data-driven organization whose dollars work smarter when migrating to the cloud.
5. Snowflake and AtScale, A Perfect Fit
No marriage is perfect, but our relationship with Snowflake comes pretty close to it. With our technology, we help you “further define multiple engines for different BI workloads, that might not auto-suspend to auto scale, but you can control the type of compute power is necessary and not just one size fits all.” Learn what else we’re doing to make Snowflake customer’s move to the cloud more secure, agile, and easily adoptable.
What do you want to see from us in the new year? Let us know!