More than ever, organizations are investing in cloud data warehouses and data lakes to make the most of their valuable data assets and deliver on the promise of agile analytics and actionable business insights. However, the process of identifying and moving data into a data warehouse or data lake is not always straightforward, all too often inhibiting progress and success.
We’ve pulled together our top blogs to offer some food for thought and guidance on core cloud data warehousing challenges, opportunities, best practices, and success stories.
Marc Andreesen famously said, “software is eating the world.” It was true then, and even more so today. The same could be said about data. Nine years after Andreesen’s famous quote, our survey of 500 organizations in the US and UK underscores that organizations are still trying to get a handle on the best way to manage their evolving data challenges. Find out what those challenges are and how to solve them.
The emergence of cloud data warehouses has transformed the way data is prepared for analytics. As cloud data warehouse adoption has grown, IT, as well as business teams, are looking for ways to load data quickly into cloud data warehouses and accelerate analytics. An Extract Load Transform (ELT) architecture addresses this market demand. There are several tools in the market that can load data into a cloud data warehouse. As you look to identify a data loader that will work best for you, here is a helpful list of 7 key things you should consider.
SnapLogic is an agnostic cloud integration platform as a service (iPaaS) that ties together and orchestrates data flows between on-premises apps and data, cloud SaaS applications, and a variety of cloud data warehouses. As such, we’re often asked for our opinion on which cloud data warehouse is the best? Whether you’re considering Amazon Redshift, Google BigQuery, or Snowflake, here’s what you need to know about their pricing models.
Moving from a physical store to a digital one can open up enormous opportunities for retail establishments. However, an e-commerce company can only thrive when it has robust analytics processes and the right tools in place to sift through billions of daily data-driven events and generate actionable insights. To tackle the complex problem of loading data efficiently into a data warehouse, organizations need a robust data loading tool that can move data from a variety of data sources such as SaaS applications and databases without writing any code or SQL so that more people in an organization can build, manage, and maintain these data pipelines.
When your business is data and you’ve accumulated it for a century, getting past data silos and disconnected applications and systems can seem nearly impossible. There comes a point in every organization where the ability to connect and glean insights from all the data your business is generating is a make it or break it moment. Kaplan Test Prep decided to look for a modern, easy to use integration platform as a service (iPaaS) that could help them obtain data on student performance, customer issues, product usage, and corporate financials. They also wanted a partner that would complement and support their digital transformation journey and big data strategy. They chose SnapLogic.
As part of their strategic cloud initiative, Pitney Bowes needed to enable internal stakeholders with data from many disparate sources. Previously, stakeholders would share and download data reports onto their local machines for data analysis. However, the data was stale as soon as it was downloaded to their machines and only painted a limited and static snapshot of the state of the business, making it virtually impossible for stakeholders to make real-time, holistic business decisions on up-to-date data. See how they solved this with Snowflake and SnapLogic.