The inaugural edition of the Talking Data podcast for 2018 features James Kobielus, analyst, Wikibon, who helps us take the racing pulse of data today. AI, machine learning, deep learning and analytics all come in for consideration. Buckle your seat belt, listen to the podcast and get ready for another tumultuous ride down the big data slope. – Jack Vaughan
Machine learning was the big story of 2017, and we here at SearchBusinessAnalytics spent a lot of time talking with businesses who use the technology.
In this edition of the Talking Data podcast, we recap some of the best interviews we did on the topic. The interviews look at everything from the role of engineers to avoiding black box functionality in models.
Talking with people who actually use a given technology is generally one of the best ways to learn about its real importance, and we spend a lot of time trying to get this perspective from our stories. As we bring 2017 to a close, we hope you can learn something useful from our efforts to drive your machine learning initiatives into the new year. Happy holidays!
As 2017 winds down, we invite you to take a look behind the big data curtain. There, you will find data engineers, data scientists, end-users and others working to move a big data concept into production. It doesn’t take much digging to find that more self-service capabilities are needed at each stage in the data life cycle.
That is among the take-aways from this latest edition of the Talking Data Podcast. In this and a subsequent episode, Ed Burns and I discuss recent user stories that graced the editorial pages of SearchBusinessAnalytics.com and SearchDataManagement.com – ones that speak to some of the outstanding trends of the year just winding down.
One of the telling threads we found was self-service; that is, self-service as it relates to ETL, as it relates to interactive data queries, and as it relates to cluster configuration. In the latter case we have as example restaurateur Panera Bread. The chain is among the company’s with particularly aggressive web initiatives underway.
More and more, when lunchtime arrives, incoming orders come in via cell phone. That can stress operational systems. Aware of this threat, Panera Bread built a Spark-Hadoop system to analyze computing needs for the processing involved in handling the lunchtime crush. It was the first in a series of Hadoop apps that Panera is spinning up quickly, after deciding to use automated container configuration software.
Panera announced earlier this year that annual digital sales had gone past $1 billion, and that projected digital sales could double by 2019. The ability to let individuals spin up big data jobs at will become handier going forward, one of the company’s engineering leads said.
Self-service that empowers more individuals in the data pipeline is a fact of life that IT has generally come to accept. It seems now to be a big part of moving at the speed of innovation. Listen to this podcast and feel free to come back for seconds.
Tableau currently has a comfortable relationship with a number of data preparation vendors, most notably Alteryx. But that hasn’t stopped the popular data visualization vendor from developing its own self-service data preparation tool, known as Project Maestro, set to be released before the end of the year. So what does that mean for Tableau’s data prep partnerships?
We explore that question in this edition of the Talking Data podcast. We look behind the news to think about how it could ripple throughout the world of analytics software. Will customers still look for standalone data preparation software when they can access good-enough functionality in the higher level software they already own? Will the secondary features of data prep software, like reporting and predictive analytics, be enough to entice customers?
For now that all remains up in the air, but we look at the questions in this podcast to see how they may play out. There are still more questions than answers in the still-hot analytics software market, and much remains to shake out.
You push the little valve down, and the music goes round and round. Where does it come out? What does that have to do with PowerBI?
Say all you want about deep learning, machine learning and neural networks – eventually enterprises are going to generate reports and dashboards for analytics. In the end, that is where “the music” comes out.
For Microsoft that dashboard and reporter often take the form of PowerBI. This analytics visualizer is an important part of the company’s analytics effort, and one of the earliest examples of its rebirth as a “cloud first” software provider.
But, although Power BI Report Server has its feet in the clouds, it is also striving to play an on-premises role. At the recent Pass Summit 2017 in Seattle, Microsoft enhanced that offering with scheduled data refresh, Direct Query, and a REST API capabilities. All this was part of the discussion on this edition of the Talking Data Podcast.
Tune into the Talking Data Podcast, as we report from Pass Summit on Power BI, and other Microsoft technology initiatives.
Cloud, automation and security were primary among a slew of topics at Oracle OpenWorld 2017. In this podcast, recorded at the event, David Essex, Brian McKenna and I share impressions on the company, and react to Oracle leader Larry Ellison’s various comments on databases, machine learning and data breaches. In the cloud, Oracle may still be playing catch-up, but it also seems to be exhibiting considerable momentum, according to the Talking Data podcasters.
We closed out September with a nod to our recent Talking Data Podcast. It is a look at digital disruption, Hadoop, and S3. The stuff that dreams are made of, as Ed Burns and I encountered them at the Big Data Innovation Conference. Be our guest – take a gander! -Jack Vaughan
Data science is nothing new, but despite the fact that it’s been around for years, businesses are still looking for ways to get value from it.
There’s an inherent tension between the research mindset required to perform good data science and the results-based focus of business processes. But by bending towards each other both areas can benefit.
In this edition of the Talking Data podcast, we recap some perspectives on how businesses can derive value from data science as presented at the Big Data Innovation Summit in Boston. One of the keys is to make sure that data science efforts serve the business. Projects don’t need to be perfect. As soon as data science team glean any kind of insight from data they should report it to business teams.
But that’s just the tip of the iceberg. Listen to this podcast to learn more about data science and business teams can better mesh their efforts and deliver real value to their enterprise.
The data side of Microsoft will be front and center at the upcoming Ignite conference, scheduled for Sept. 24 through 29 in Orlando, Fla. Sessions at the event will flesh out important details concerning SQL Server 2017 for Linux, Azure SQL Data Warehouse and the company’s most recent NoSQL database entry — Azure Cosmos DB. The Redmond giant has increasingly used SQL Server as a launching pad for analytics efforts that have come to rival those of database; one such effort is new Python support for the data warehouse, which also will be among the topics considered at Ignite. Catch this version of the Talking Data podcast, which looks back at this summer’s Microsoft data doings, and forward to the conference.
In this podcast, we get a chance to speak with Neha Narkhede. As a development lead at LinkedIn, she helped forge the messenger called Kafka. Now as CTO at Confluent, she is charting the course of Kafka as a streaming engine. Part of that is Exactly Once messaging, which will be among the items on tap at Confluent’s upcoming Kafka Summit. Why do they call it Kafka? We are not telling here. You must listen to the podcast to learn.