The Warehouse

The manufacturing industry is undergoing a digital transformation, and one of the key technologies driving this transformation is cloud analytics. Cloud analytics refers to the use of cloud-based data analytics platforms and tools that allow one to analyze substantial amounts of data quickly and easily. This can help manufacturing organizations to gain insights from their data and make more informed decisions.

There are several reasons why manufacturing organizations should consider adopting cloud analytics. First, cloud analytics can help manufacturing organizations to improve their operational efficiency. By analyzing data from across their operations, manufacturing organizations can ident...

Covid-19 had significant impact in the advancement of digital transformation. We witnessed the need for companies to be more agile and transform their traditional operating model. Post-pandemic these companies still feel a new set of pressure on the business to continue to innovate, develop a new customer experience, and improve operational efficiency while maintaining an acceptable level of output. Now more than ever, CIOs and Leadership need to collaborate and look to the unique advantages of cloud, data, and analytics as they build their digital efficiencies and IT roadmaps.

How did the Pandemic impact the manufacturing industry?

The COVID-19 pandemic has had a major impact on the manufac...

By: Jorel Digman

November 9, 2022

Data and Analytics projects can be complex and time-consuming, especially for an internal team with additional responsibilities. It requires the right technical skillset to realize your data’s full potential and see the benefits of a modern data stack built in the Snowflake Data Cloud.

Working with a Snowflake partner may be ideal when there is a lack of internal Snowflake Data Cloud expertise. A certified Snowflake partner can help you with data initiatives of all sizes and complexities whether you’re looking to deploy a data lake or data warehouse, improve existing data architecture and pipelines, or implement custom ML workflows with Snowpark.

1. Sno...

Pandata Group, a growing provider of cloud-native data and analytics services, has opened its newest office in Cincinnati, Ohio. The expansion is focused on helping regional clients fuel their digital transformation initiatives with exceptional consulting and professional services in data and analytics transformation.

Ohio’s business climate, industries, and geographic location are all viewed as an ideal fit for the Pandata Group story and it’s 3-year strategy to grow across the Great Lakes Region. “Pandata Group is a very special company. We’re highly focused on delivering best-in-class cloud technology to our customers, and on building a vibrant, client-centric cult...

As enterprise data volumes continue to grow and the velocity of data change increases, companies are more challenged than ever to provide timely and complete data sets for business analysis. Change Data Capture is a methodology that has been employed for some time to effectively extract changes into target data environments in near-real time by avoiding regularly scheduled batch cycles and loading source data as it updates. The objective is to have a provisioned data store be as reflective of the current source system data as possible. There are several approaches to CDC employed by data engineers, but in the end, which is the most effective and least costly strategy that insures complete ...

In the first installment of this series, we examined how mid-sized enterprises can quickly get started on their journey to data maturity by implementing an operational reporting platform in as little as 4 to 6 weeks. The target users for this type of data service are the mid-line operational managers looking for actionable, tactical insight into system operations. The next user group to reach on the data maturity journey are the decision-makers at the departmental level (finance, sales, marketing, supply chain, etc) who require strategic insight for planning and resource management. The architecture required for this next level of data analytics is the 2-tiered subject-oriented data warehou...

Today’s businesses continue to strive for data maturity and to create a culture that is data-driven and data-literate. But the perception remains that starting such a journey is expensive and time-consuming. There is a belief that a combination of high upstart costs and lengthy implementation time prevents the business from seeing any near-future value in its data and analytics investment. The reality, however, is that today’s cloud-based data platform technologies, specifically the triumvirate of Snowflake (data storage and compute), Matillion (data loading and transformation), and ThoughtSpot (data analytics and insight), enable rapid analytic ability with minimum initial inv...

The time it takes from beginning a data cloud initiative to realizing any of tangible value can be years.

After all, there is a lot to consider. First you need to ensure that there is business alignment, between the business and I.T., on what the short, mid, and long-term objectives are. Second, there is the small task of assessing your entire data estate. It is important to figure out with each vendor which modern data stack configuration, workload option, availability zone, and pricing plan is the optimal configuration for you.

Third, you need to build a migration plan, defining a strategy for each possible candidate analytical application and data set, that will drive value to the business ...

One of the challenges that many Snowflake administrators face is the daily monitoring of user and resource activity in their environment. The advantages of Snowflake's consumption-based pricing model (instantly-scalable compute sizing, usage-focused scheduling, etc.) are best employed when compute and storage activity is transparent to the business. Snowflake includes an ACCOUNT_USAGE view-based schema in the out-of-the-box Snowflake database than contains all the information related to account activity. This data can, of course, be directly queried like any other data, but wouldn't it be nice to have a single view of key metrics around data storage and activity that can be monitored day to ...

Pandata Group has always embraced and promoted the effectiveness of a robust data model as a means of “mapping” the enterprise data landscape (see our blog post here). Today this is more important than ever in the increasingly expanding cloud-native data analytics universe. A good data model provides not only a guideline for data engineers, but also a means of codifying and standardizing data usage from a governance perspective. Seamless integration of a data modeling tool into a cloud-native data management platform increases efficiency, collaboration and transparency. This is why we are so excited to be named a Gold Partner with SqlDBM, the leading cloud-based modeling solution...