WEBINAR: Modernise Your Data Warehouse - 22 March 2022

 

Hi there, Hundreds of companies have already moved their traditional ETL and data warehouse workloads to the Databricks Lakehouse Platform in the cloud. Join us at the Modernize Your Data Warehouse Webinar on March 22, 2022, at 10 AM PT to learn more about why the lakehouse is your next data warehouse — and to better understand the architecture, customers’ modernization journeys, and how to plan and execute your data warehouse migration to Databricks. In this webinar, we will cover: How Databricks experts can help you plan a comprehensive EDW and ETL migration process How to implement your migration and highlight some key migration partners Live Q&As throughout the presentation

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WEBINAR: Improving Your DataOps Practices with Data Fabric - 16 February 2022

 

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Webinar: Improving your DataOps Practices with Data Fabric Today's enterprises struggle to unlock real value out of data that is stuck in legacy relational databases, data warehouses and data lakes. Modernizing disparate data silos to a single data fabric is key to managing change over time and creating data-driven innovation and value. Join this session to learn how you can build a modern data fabric leveraging the Lumada DataOps Suite from Hitachi Vantara along with MongoDB Atlas. In addition, hear from a utility customer that is reaping the benefits of the data fabric and see a demo of how you can create a modern data pipeline to drive great enterprise data value. Register to watch this webinar live when it airs February 16th. Register Now Hope to see you there, Sean Welch Data Science Central

WEBINAR: How SlickDeals Delivers Analysis-Ready Data from its Snowflake Data Warehouse to Support Business-Critical Decisions - 25 February 2021

 

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Thursday, February 25th, 2021 | 1PM ET / 10AM PT   Join us for a webinar conversation with SlickDeals’ Data & Analytics Director, Greg Mabrito, to learn how he is building a cloud analytics platform to deliver dimensional analysis for business-stakeholders to provide more accurate reporting, in a fraction of the time. You’ll learn how SlickDeals: Modernized their analytics infrastructure to support ad-hoc reporting demands of business stakeholders. Deliver analysis-ready data to analysts to ensure timely and accurate decision-making. Moved away from SSAS while maintaining a business semantic layer and dimensional analysis capabilities on data in Snowflake.   REGISTER NOW OUR SPEAKERS     GREG MABRITO Director of Data and Analytics, Slick Deals   DAVE MARIANI Chief Technology Officer, AtScale

WEBINAR: Cloud Data Warehouse Automation at Greenpeace International - 10 December 2020

 

Data Science Central Webinar Series Event

Cloud Data Warehouse Automation at Greenpeace International Join us for this latest DSC Webinar on December 10th, 2020 The cornerstone of fleet management is maritime reports, which record everything that has happened onboard any given ship in the past 24 hours. The difficulty of quickly analyzing these reports is that each captain sends different data, resulting in a complex mixture of Excel, Google Sheets and applications accessible via APIs. The solution? An end-to-end automated cloud data warehouse that allows Greenpeace to ingest its maritime reports, quickly prepare them, and publish the results out in a simple consumable format. Torbjorn Zetterlund, Digital Innovation Manager at Greenpeace International, manages the entire process single-handedly. Despite the complexity of the data problem at hand, Zetterlund has been able to leverage this solution to vastly improve the speed and accuracy of mission-critical reporting. In this latest Data Science Central webinar, you will learn from an experienced practitioner on: Best practices for building an end-to-end cloud data warehouse (data collection, data preparation and reporting) How to turn disparate and inconsistent data into clean and normalized data that’s ready for reporting How to automate a data pipeline (from connected and disconnected sources) and manage the unexpected How to deliver a solution in record time with a one-man team> Speakers: Bertrand Cariou, Sr. Director Product - Greenpeace Torbjorn Zetterlund, Digital Innovation Manager - Greenpeace International Hosted by: Sean Welch, Host and Producer -- Data Science Central   Title:  Cloud Data Warehouse Automation at Greenpeace International Date:  Thursday, December 10th, 2020 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

WEBINAR: Lakehouse: The future of cloud data platforms - 2 parts 22 and 29 September 2020

 

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September 22nd & 29th         09:00 am - 11:00 am PT

Hi, The Lakehouse pattern is emerging as the successor to the data warehouse and data lake because it combines the advantages of both - with none of the shortcomings. What is Databricks' vision for Lakehouse? You're invited to a 2-part series where you'll learn what's driving the development of the Lakehouse pattern and Delta Lake. Part I: Lakehouse: The New Approach to Managing Data Part II: Leveraging Delta Lake for High-Performance SQL and Analytics You'll learn: The challenges facing managing data in the cloud Why Databricks' vision for Lakehouse sets it apart How Delta Lake and Delta Engine bring high-performance to SQL and analytics

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I Have a Data Warehouse, Do I Need a Data Lake Too? by Troy Hiltbrand via @TDWI Transforming

When building your data and analytics program, you must decide whether you need a data warehouse, a data lake, or both. Understanding the difference is the first step.

Important to understand the difference and this is very useful to help you understand it.

Presto-powered S3 data warehouse on Kubernetes by @joshua_robinson via @Medium

Joshua Robinson offers up a tutorial on how to set up a Presto data warehouse using Docker that could query data on a FlashBlade S3 object store, and a follow-up tutorial that explains how to move everything, including the Hive Metastore, to run in Kubernetes.

This is very useful to read and might help you to achieve something quicker than you have planned.

Morgan Stanley focuses on data quality to strengthen AI by Penny Crosman via @infomgmt

The bank is one of many to realize that artificial intelligence is only as good as the data fed into it.

They have it completely right - you absolutely cannot rely on answers if the underlying data is wrong in any way.

How natural language processing can transform business intelligence by Alberto Pan via @infomgmt

NLP has the potential to enable true self-service BI by seamlessly translating spoken commands into SQL or any other technical query language. 

I great read which I really enjoyed.

Data Warehouse or Data Lake? When to Use Each by @LimiMaayan via @Datafloq

What platform should you use to power your data analytics machine? Data warehouses and data lakes are common two alternatives.

Some thoughts to help you decide between the two. Many analytics rely on the data having some kind of structure which points more towards a data warehouse, but I can also see that you can discover new things when you do more with unstructured data (data lake). It really depends on what you are trying to do.

Successful data management strategies start with quality control by Martin Doyle via @infomgmt

There is the obvious waste in data, but it’s the bad decisions - when based upon false information - made with a high degree of certainty that do the real damage.

Bad quality data gives you results that you cannot rely on - if you can't rely on it what is the point doing anything with it?  I certainly wouldn't risk my business on decisions made with bad data.

Three trends that will help organisations modernise their data warehouse by David W. Thompson via @infomgmt

The adoption of machine learning and the need for access to data beyond just data science team is changing how many firms approach information warehousing.

Interesting thoughts from David. I don't think data warehousing is dead, but it'a certainly not in the same form as it was 10 years ago.

Why Big Data Enhances the Need for Enterprise Information Management by @s11ravindra via @datanami

Many EIM (enterprise information management) or data management programs do not live up to their potential, and the arrival of big data makes the need for enterprise information and data management even more significant.

The article makes a number of great points and I have to agree with him. It all sounds great and sounds like an ideal set of systems to work on. I think this could give you the start of a blueprint of what needs to be in place for success.

What is the Benefit of Modern Data Warehousing? by Ronald Van Loon via @simplilearn

Access to relevant customer and industry information is the primary competitive advantage businesses have over their direct and indirect competitors today. It’s the smartest approach to remaining vigilant in a business environment where competition is at an all-time high.

There is definitely still a home for a data warehouse, however you need to really understand why you are using one and what benefits it will give you.

Organisations take data warehousing to the cloud by David Weldon via @infomgmt

A growing number of firms are attracted to promises of unlimited scalability and low cost storage.

I find the concept of having a data warehouse in the cloud quite exciting.  If you think about it, I can see that it can make economic sense to have it stored this way.

SLIDESHOW: Top 10 Big Data Trends We’ll See in 2017 by Joe Caserta via @infomgmt

Last year was the year of ‘big data.’ This will be the year of ‘data intelligence,’ as organizations look for actionable insights from all that data. Here are 10 trends to expect.

Interesting list.

Data Lakes Hold Great Promise, But Huge Challenges for Many Firms by David Weldon via @infomgmt

A growing number of companies are exploring data lakes, but many struggle with how to turn raw material into actionable insights, according to Rich Dill, an enterprise solution architect at SnapLogic.

I think that you could potentially find all sorts of things if you a) had a lake/repository containing just data and b) had the imagination to think of a question to ask.

SLIDESHOW: 7 Key Considerations When Choosing a Data Pipeline Service via @infomgmt

Picking a service that manages your data isn’t something to be taken lightly. You’ll want to research a few different services before choosing what is right for your company. Here is advice on how to look at different services to make sure you really understand the value each brings to the table.

I find slide 9 quite important even though the sideshow says it is a bonus.  In the past data has been put into a data warehouse or whatever is driving your reporting and just sits there forever.  All data has a shelf life and needs to be moved, updated or archived off in a timely manner else you run the risk of your reports and analyses being incorrect.

SLIDESHOW: 5 Steps to Maximizing the Value of Your Big Data Lake via @infomgmt

Big data lakes have created a lot of change, a lot of angst and most importantly -- a lot of opportunity, according to Avi Kalderon, big data and analytics practice leader at NewVantage Partners. Here are five ways in which you can maximise the value of your data assets.

Interesting comments.

4 Ways to Shrink the Gap between Data Integration and Insight by Yaniv Mor via @Data_Informed

According to a study conducted last year by my company, Xplenty, nearly one-third of business intelligence professionals say they spend between 50 and 90 percent of their time just cleaning raw data for analytics. As a result of valuable time and talent devoted to preparing data, businesses are often slow to unlock its insights or to act on them.

Good to be reminded of these.