Recently, I worked with a large fortune 500 customer on their migration from Apache Storm to Apache NiFi. If you’re asking yourself, “Isn’t Storm for complex event processing and NiFi for simple event processing?”, you’re correct. A few customers chose a complex event engine like Apache Storm for their simple event processing, even when Apache NiFi is the more practical choice, cutting drastically down on SDLC (software development lifecycle) time.

Earlier this year at Snowflake Summit 2021 , we announced Snowpark Accelerated , a new program for partners who integrate with Snowpark. It provides them with access to technical experts and additional exposure to Snowflake customers. It’s been incredibly exciting to watch what our partners have been building with the help of our new developer experience, which brings deeply integrated, DataFrame-style programming to the languages developers like to use.

The story of the last year+ is one of disruption and change across every aspect of our lives. As we all navigated a ‘new norm,’ businesses naturally had to pivot as well, with some sectors finding new opportunities while others scrambled to reimagine their entire go-to-market strategies.

BigQuery is capable of some truly impressive feats, be it scanning billions of rows based on a regular expression, joining large tables, or completing complex ETL tasks with just a SQL query. One advantage of BigQuery (and SQL in general), is it’s declarative nature. Your SQL indicates your requirements, but the system is responsible for figuring out how to satisfy that request. However, this approach also has its flaws - namely the problem of understanding intent.

Simone Clancy, Director of People Strategy at Yellowfin, and Jessica Maree, Program Director at VICT4W, had the opportunity to interview each other about leadership and mentoring, and share some valuable lessons they've learned.

When Apple launched its App Store in 2008, it opened a window of opportunity for thousands of software developers who rushed to invent the mobile-first world in which we all live today. Vendors that embraced the new paradigm, such as game developer Imangi, saw breakthrough success.

“More than 15 billion IoT devices will connect to the enterprise infrastructure by 2029.” Finding data is not going to be a challenge, clearly, but taking advantage of it all to drive business outcomes will be. Combining AI and machine learning (ML) with data collection and processing capabilities of the edge and the cloud may hold the answer.

Both businesses and governments have been forced to respond to the global pandemic by developing interactive user experiences and spatial applications using location-based data sets to visualize COVID cases, communicate confinement measures, and track vaccine rollout progress.

Over the past few years, many organizations have experienced the benefits of migrating their SAP solutions to Google Cloud. But this migration can do more than reduce IT maintenance costs and make data more secure. By leveraging BigQuery, SAP customers can complement their SAP investments and gain fresh insights by consolidating enterprise data and easily extending it with powerful datasets and machine learning from Google.

With this first article of the two-part series on data product strategies, I am presenting some of the emerging themes in data product development and how they inform the prerequisites and foundational capabilities of an Enterprise data platform that would serve as the backbone for developing successful data product strategies.