Many organizations have mountains of unused data stored in Snowflake. This data potentially contains treasure troves of valuable insights, but organizations may lack the resources to use it to its full potential. One way to expand use of these underutilized data resources is to empower nontechnical users in your organization to work with Snowflake data – without writing code.

In this episode of DIY Data, we’ll walk through two different approaches to no-code data preparation and show how anyone, regardless of background, can turn raw data into usable, actionable data products

Want to deepen your ability to collaborate across business users and IT? In this episode of DIY Data, we’ll show how Designer Cloud makes it easier than ever to accomplish collaboration across the organization. Join us to learn how business users with special domain knowledge can leverage Designer Cloud’s no-code user interface to prepare their data, while collaborating with skilled engineers to assist with things such as the creation of new data connections and the setup of proper execution environments.

Looking to deepen your data automation & orchestration? Designer Cloud brings the opportunity to schedule unlimited, parallel orchestration across your data workflows.

Join this session to learn how to enhance your automation capabilities with the benefits of unlimited orchestration across the cloud. We’ll discuss how you can create complex automation sequences, including conditional execution, parallel execution, and webhooks to external APIs.

Through two seasons of DIY Data, we’ve shown how Alteryx Designer Cloud (formerly Trifacta) enables self-service data engineering across several different use cases and architectures, while also demonstrating how Designer Cloud makes it easy to deliver enterprise-grade data pipelines.

In this season finale episode, we’ll tie it all together by showing how data engineers and analysts can collaborate together on a data analytics job within Designer Cloud, leveraging individual strengths to deliver a fully functioning data pipeline in minutes rather than hours. While working alongside one another, we’ll show you how:

• Data analysts can leverage their domain knowledge to prepare and transform the data using no-code predictive transformation suggestions and an intuitive user interface
• Data engineers can easily build connections to data sources and schedule the automation of data pipelines

Ready to see how Designer Cloud is uniting teams to achieve insightful analytics? Let’s go!

Code will always play a pivotal role in data pipelines. But even for skilled practitioners, writing code takes time – and brings the possibility of human error. For increased accuracy and efficiency, it makes sense to do as much no-code/low-code data engineering as possible – even if you’re code-proficient. But how much can you really do without code? More than you might think!

In this episode of DIY Data, Amit Miller, Data Wrangling Engineer, will demonstrate how data practitioners can build and orchestrate fully-functional data pipelines, from ingestion to delivery, without writing a single line of code. Watch as we use nothing but machine-learning-guided clicks to join datasets, transform data, and deliver insights to downstream applications to drive intelligent business outcomes. It’s simple, quick, and efficient!

Ready to start transforming your data with us? Let’s go!

In this episode of DIY Data, you’ll discover how you can prepare complex JSON data files with a few simple clicks through a technique known as “brushing.” We’ll demonstrate different aspects of data transformation, including extracting values in a query string, splitting values, replacing values, and filtering data based on your criteria.

How does brushing work? Just highlight the exact piece of data you want to transform and instantly get a ranked set of intelligent transformation suggestions that are automatically generated based on the selected data. Ease of use, speed, and accuracy are the hallmarks of Designer Cloud’s data engineering platform, with techniques such as brushing allowing you to perform complex transformations with a few simple clicks.

As data architectures evolve towards the modern data stack with ELT approaches, the difficult work of transformation (the T in ELT) is pushed onto the cloud data warehouses (CDW) to leverage the scale and power of these data repositories. But it’s easier said than done as data permeates from everywhere through disparate sources, making it cumbersome to push the transformation workload into your CDW without compromising on performance. Well, not anymore as Designer Cloud makes it possible with a solution that gives you flexibility, ease-of-use, speed, and performance.

In the season finale of the opening season of DIY Data, you’ll learn about the innovative Pushdown Optimization techniques from Designer Cloud that is designed to harness the power of the CDW achieving data transformation through a SQL-based ELT approach. We’ll demonstrate our latest Pushdown Optimization capabilities on Snowflake in the AWS cloud, where we’ll generate SQL scripts from Designer Cloud recipes delivering intelligent, high-quality, and AI-driven data transformations. You’ll also learn how this solution complements our pushdown abilities on other CDWs such as Google BigQuery.

Data engineering has evolved over the years to include various techniques such as ETL, ELT, or even custom code. Do you need to move your architecture to meet your data engineering needs? The answer is No. Designer Cloud offers you complete flexibility with an open and interactive approach to retain your data architecture and meet your data engineering requirements.

In this third episode of Do-It-Yourself Data, How Designer Cloud Fits into Your Data Architecture- ETL, ELT, Custom Coding, Director of Partner Solutions at Alteryx, Vijay Balasubramaniam, will dive deep into these different approaches and demonstrate:

• How Designer Cloud can support all three architectures: ETL, ELT, and Custom Code
• How to mix and match these architectures in Designer Cloud
• How to develop and orchestrate robust, automated data pipelines that combine the power of Designer Cloud’s interactive, visual interface while integrating with SQL and Python code

Delivering high-quality analytics requires scale, ease of use, and accuracy.

In this second episode of Do-It-Yourself Data, How to Clean, Prepare, and Transform Data for Marketing and Sales Analytics, you’ll learn how to use pre-built templates from Trifacta to generate the right analytics for marketing and sales.

Director of Saas Adoption and Development at Alteryx, Connor Carreras, will demonstrate how you can convert raw data from a typical CRM application into usable data for insights. With a ready-made template, you’ll learn how Designer Cloud enables you to:

• Connect to your data
• Clean and transform raw data by standardizing data values and dropping unnecessary attributes
• Perform the required calculations for analysis

Connor will also show you how to use flows and recipes within Designer Cloud for advanced insights and analytics.

Ready to start transforming your data with us? Let’s go!

Whether you are a Fortune 500 company or an emerging startup, data is the most relevant entity to build workflows and get advanced insights into your business. But, how do you build a data workflow that can be open, flexible, and scalable to keep up with the demands of the business and help obtain the required analytics?

Welcome to DIY Data from Alteryx! In this opening 30 minute episode of Do-It-Yourself Data, Analytics in the Cloud: Enabling the Modern Data Stack, we’ll focus on building efficient data workflows with the Modern Data Stack, an intelligent cloud-based framework that brings together data ingestion, data warehousing, and data analytics.

Senior Data Engineer, Misagh Jebeli, will show you how Alteryx enables the Modern Data Stack with practical examples of:

• Data on-boarding
• Pushing to the cloud data warehouse
• Achieving analytics at scale for your business

Ready to start transforming your data with us? Let’s go!