Among the topics we covered: 

• I know that Oracle is working to make it easier and cheaper to use multiple cloud providers. In a larger sense, what trends do you see driving the relationship between the database and multi-cloud computing this year?
• Let’s talk about the news. Oracle will announce that AWS developers will have a choice to run transaction processing, analytics, and machine learning within a single database. What is the larger significance of this?
• What about MySQL Heatwave, including the evolution of the solution?
• What about the benchmarks – how does Oracle compare with other cloud database vendors?
• The future of the database? There’s a lot happening in the database market these days. What are some key trends that are moving the sector forward?

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The post Oracle SVP Nipun Agarwal on MySQL HeatWave appeared first on eWEEK.

• As you look at the market adoption of serverless databases, what’s driving it forward?
• How will serverless databases change the way that developers work with databases? What about cloud native?
• How is Cockroach Labs addressing the database needs of its clients? What’s the Cockroach advantage?
• The future of the database and the database market? What are some key milestones we can expect in the years ahead?

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The post Cockroach Labs’ Jim Walker on Serverless Databases appeared first on eWEEK.

• Let’s talk about the report – but first, let’s clarify. The report mentions the importance of data literacy. What exactly is data literacy? How can or should companies quantify it?
• The report says that data training is key to fighting the Great Resignation. What else do you see as particularly important about the report?
• Data training is vital to employee success and retention. How can companies prioritize it?
• The future of data literacy? It appears that the data literacy of workers is growing rapidly.

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The post Tableau CTO Andrew Beers on the Rising Importance of Data Literacy appeared first on eWEEK.

Among the topics we discussed:

• What’s your sense of how companies are navigating the challenges of data analytics these days? Do you think they’re relatively effective, or are companies generally struggling?
• If you’d give advice to companies about how to get more from their analytics software, what would you recommend?
• What’s a tool or application from the SAS portfolio that’s particularly notable? What makes it stand out?
• What do you see as the near to mid-term future of data analytics? What milestones do you expect over the next few years?

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The post SAS’s Kimberly Nevala on How to Optimize Data Analytics for Success appeared first on eWEEK.

Especially when it comes to migrating an enterprise data warehouse, horror stories abound. Failed migrations that collapsed after three years of hard work are quite common. Migration projects that cost over $20 million before they fell apart are the norm. But ask any IT leader off-the-record and you might learn about much costlier disasters.

As enterprises move to the cloud, modernizing on-prem data warehouses to cloud-native technology is a top priority for every IT executive. So, what is an IT leader to do? How can enterprises avoid migration disasters when moving legacy data warehouses to the cloud?

Over the past year, several vendors brought to market the concept of database virtualization. The principle is quite simple. A virtualization platform lets existing applications run natively on the cloud data warehouse. No or only minimal changes of SQL are required. So, how does database virtualization take the sting out of database migrations?

What is database virtualization?

Think of database virtualization as Hypervisor technology for database queries. The database virtualization platform sits between applications and the new destination data warehouse. Like any virtualization technology, it decouples two otherwise tightly bonded components. In this case, database systems and applications are abstracted from each other.

The database virtualization platform translates queries and other database statements in real-time. In effect, database virtualization makes a cloud data warehouse like Azure Synapse behave exactly like a Teradata or Oracle system. This is quite different from data virtualization. Data virtualization implements a new SQL dialect and requires all applications to be rewritten to this dialect first.

Compared to static code conversion, database virtualization is significantly more powerful. It can emulate complex constructs or data types. Even elements for which there is no equivalent in the new destination database can be emulated in real-time.

Applications originally written for one specific database can now run on a different database without having to change SQL. Instead of static code conversion with all its risks and challenges, database virtualization preserves existing applications. Instead of months of rewriting application logic, database virtualization makes the new database instantly interoperable with existing applications.

Virtualization separates migrating to cloud from application modernization

Database migrations typically fail because of an explosive increase in scope while the project is afoot. It starts as a simple mission where making existing applications work with a new database is the goal. However, once it becomes clear how extensive the rewrites will be, the scope of the project often changes.

Stakeholders may view the operation as a unique opportunity to modernize their application. If the application needs significant rewrite, they argue, why not make other important changes too? What started as a supposedly minimally-invasive operation turns now into full on open heart surgery.

In contrast, database virtualization lets applications move as-is. All changes are kept to the absolute minimum. In practice, the extent of changes to applications is around 1%. With cloud data warehouse technology evolving rapidly, we expect the need for even those changes will be further reduced in the future.

Database virtualization changes the above dynamics quite significantly: move first, modernize applications afterward—and only if needed. Once the enterprise is cloud-native, a few select applications may be candidates for modernization. Separating move and modernization is critical to controlling the risk.

Virtualization overcomes the dreaded 80/20-nature of migration

No other IT problem is so often underestimated. We attribute the error in judgement primarily to the fact that it is an incredibly rare operation. Most IT leaders have never planned, nor executed, a major database migration. If they could help it, they made database migrations their successor’s problem.

Once a rewrite project is underway, the initial progress can be exhilarating. Within just a few weeks, the “easy” SQL snippets are converted rapidly. Many just need substituting a few keywords and similarly trivial changes. In true 80/20 fashion, the first 80% take up only very little time and almost no budget. Then comes the last 20%. This is where hard problems are—and disaster strikes.

In contrast, database virtualization does not distinguish levels of perceived difficulty. Instead, progress is made uniformly. This is not to say there are no challenges to tackle. However, compared to code conversion, the effort needed to overcome those is typically an order of magnitude smaller.

Virtualization mitigates risks

Conventional migration is a high-risk undertaking. As we’ve seen, it starts with underestimating the effort needed. The limitations of rewrite-based approaches are impossible to know up front, despite best efforts. Yet, IT leaders are often asked to put their careers on the line in these projects.

Importantly, with rewrite-based approaches, IT is shouldering the responsibility mostly alone. They are tasked to complete the migration, and then the business gets to judge the outcome.

Compare this to database virtualization. From the get-go, applications can be tested side by side. IT signs up business units early on who can test drive their entire function using their existing tools and processes. Database virtualization promises to relieve IT from taking the risk of implementing something the business cannot use once complete.

On top of that, database virtualization comes with one rather obvious mechanism of risk mitigation. Until the old system is decommissioned, the organization can always move back to the old stack. Reverting back requires no special effort since all applications have been preserved in their original functionality.

Replatform IT to the public cloud

Major enterprises are about to replatform their IT to public cloud. However, so far only a fraction of on-prem systems and processes have been moved. The specter of database migrations is holding enterprises back as all critical workloads are tightly connected to database systems.

Database virtualization is therefore a powerful paradigm for IT leaders who are considering a database migration. While still a young discipline, database virtualization has proven its mettle with notable Global 2000 clients already. So far, its proof points are limited to enterprise data warehousing. However, little imagination is required to see how this technology could apply to operational databases as well.

Database virtualization should be viewed as a critical weapon in the IT leader’s quiver to attack migration challenges when an efficient way to migrate data to the cloud is called for.

About the Author:

Mike Waas, Founder and CEO, Datometry

The post How Database Virtualization Helps Migrate a Data Warehouse to the Cloud appeared first on eWEEK.

Among the topics we covered:

• What’s the current state of ransomware?
• How can organizations prepare for a ransomware attack in such a way that they can avoid paying the ransom?
• What’s wrong with most company’s “data as usual” procedures? What advice would you give?
• What’s the future direction for data management, and how can companies prepare for it now?

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• James Maguire on Twitter: https://twitter.com/JamesMaguire
• Mohit Aron on Twitter: https://twitter.com/mohitaron
• eWEEK on Twitter: https://twitter.com/eWEEKNews
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The post Cohesity’s Mohit Aron: Managing Data to Fight Ransomware appeared first on eWEEK.

The experts:

Kelly Symons, Senior Vice President, Data Management and Quality, Mastercard

Myles Suer, Director of Solutions Marketing at Alation, the facilitator for the CIOChat.

Gwen Thomas, Sr. Compliance Officer for the International Finance Corporation, which is part of the World Bank Group

Bob Seiner, President and Principal Consultant of KIK Consulting & Educational Services

Topics we discussed: 

1. Why is data governance such a hot topic for CDOs, CIOs, and other business leaders today?
2. Given the importance of data governance, how do executives steer clear of making it a business constraining effort?
3. What issues need to be solved to enable data governance leaders, CDOs, and CIOs to succeed at data governance?
4. Where you have succeeded at data governance, please share a specific concrete example of how it was implemented?
5. Future of data governance?

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The post The Role of Data Governance in Effective Data Management appeared first on eWEEK.

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• eWEEK on LinkedIn: https://www.linkedin.com/company/eweek-washington-bureau

The post Hitachi Vantara’s Radhika Krishnan on Data Fabric and Data Management appeared first on eWEEK.

Among the questions we discussed:

• If you look at today’s typical data storage infrastructure, what sort of problems do you typically see? Where are the pain points in data storage for most companies?
• Weka did a survey about the state of AI and analytics infrastructure. What are a few of the key findings?
• What’s your advice to companies to help improve their data storage infrastructure?
• What do you see as the future of data storage over the next few years – what role will AI play?

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Jonathan Martin, the president of Weka, discusses how today’s exponential data growth is reshaping data storage, particularly in the analytics sector.

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The post Weka’s Jonathan Martin on Enterprise Data Storage: Beyond Incremental Change appeared first on eWEEK.

1. What are the big challenges with managing and access data in multi-cloud world?

2. How do you see companies grappling with these challenges? What are some typical fixes, either successful or not?

3. Your web site says “Komprise puts you in control of your unstructured data – not your vendor – so you can know, move, manage, and harness data anywhere.” But unstructured data by its nature is hard to define, so how does Komprise do this?

4. Future of data management several years from now? What do you forecast?

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• James Maguire on Twitter: https://twitter.com/JamesMaguire
• Krishna Subramanian on Twitter: https://twitter.com/cloudkrishna
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The post Komprise Co-Founder Krishna Subramanian on Data Management-as-a-Service appeared first on eWEEK.