What Are the Hurdles Companies Face With Databases Today?
What Are the Hurdles Companies Face With Databases Today?
The three most frequently mentioned industries were financial services, healthcare, and retail; however, it is agreed there are database applications for every industry.
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Built by the engineers behind Netezza and the technology behind Amazon Redshift, AnzoGraph™ is a native, Massively Parallel Processing (MPP) distributed Graph OLAP (GOLAP) database that executes queries more than 100x faster than other vendors.
To gather insights on the state of databases today and their future, we spoke to 27 executives at 23 companies who are involved in the creation and maintenance of databases.
We asked these executives, "What are the most common issues you see companies having with databases?" Here's what they told us:
Lack of Knowledge
- The lack of education about and understanding of GPUs in the datacenter. Adoption of the cloud. Teaching enterprises about the difference in hardware technology. There are 50 to 100 different types of databases and the customer needs to understand the benefits of each type. We provide the ability to integrate TensorFlow, Café, Torch. No limits on the types of feeds, languages, and interfaces you can use. There’s a lot of Oracle and SAP legacy databases with years of stored procedures that need to be unwrapped to see changes for the better with regards to visibility and greater actionability on the data. Fusing consumer IoT with industrial IoT.
- When developers work with databases, they can quickly find themselves in over their heads if they try to address database issues. It’s only natural for a developer to turn back to their code as the most familiar path to resolve an issue. In most cases, the database engine will do a better job at finding the most efficient way of completing a task than you could in code — especially when it comes to things like making the results conditional on operations performed on the data.
- The more data that is stored is inversely proportional to a company’s ability to analyze the data. Companies have no clue how many copies of data they have. No idea of data lineage. Data virtualization helps codify the dependencies of data storage saving significant storage dollars. In-memory technology requires writing to different APIs. We’re trying to introduce a combination of data virtualization and data grids to provide a consolidated view of all of the data.
As customers deploy to next generation databases, they need automatic backup and recovery. Test and development environments need to meet the two-week DevOps release cycle. Automatically refresh data nightly to provide test and development with the data they needed.
- The need to align database changes with application changes. Stems from Conway’s Law – the company that designs the process will design the process so it follows the line of communication in the company. That mentality does not work anymore. Move to the cloud with a DevOps methodology. The database model hasn’t changed since 1979.
- Modernizing the application stack of existing applications moving from Oracle and SQL server to Cloud. How to manage the data tier. Need to modernize data tier at the same time. Architect and infrastructure change quickly how to manage databases to let me change over time.
- Depends on if you are working with a third-party app versus build your own. Build your own need to be optimized. We help the third party with the infrastructure to help with performance and disaster recovery.
- Larger disparate teams needing to integrate databases into DevOps. Enable to speak the same language as the application development team. Provide different tooling so they are able to plug the databases into the processes that exist using the same technology. Shift the database integration process left.
- Adapt to changing infrastructure – cloud and containers. Different use cases serving different requirements. Intelligent payment processing 24/7/365. Different requirements for each use case. Understand how to make the database meet the requirements. Consistency, persistency, partition tolerance. What’s the best way to make the database meet the requirements?
What are some hurdles you see companies facing with databases today?
Here’s who we talked to:
- Emma McGrattan, S.V.P. of Engineering, Actian
- Zack Kendra, Principal Software Engineer, Blue Medora
- Subra Ramesh, VP of Products and Engineering, Dataguise
- Robert Reeves, Co-founder and CTO and Ben Gellar, VP of Marketing, Datical
- Peter Smails, VP of Marketing and Business Development and Shalabh Goyal, Director of Product, Datos IO
- Anders Wallgren, CTO and Avantika Mathur, Project Manager, Electric Cloud
- Lucas Vogel, Founder, Endpoint Systems
- Yu Xu, CEO, TigerGraph
- Avinash Lakshman, CEO, Hedvig
- Matthias Funke, Director, Offering Manager, Hybrid Data Management, IBM
- Vicky Harp, Senior Product Manager, IDERA
- Ben Bromhead, CTO, Instaclustr
- Julie Lockner, Global Product Marketing, Data Platforms, InterSystems
- Amit Vij, CEO and Co-founder, Kinetica
- Anoop Dawar, V.P. Product Marketing and Management, MapR
- Shane Johnson, Senior Director of Product Marketing, MariaDB
- Derek Smith, CEO and Sean Cavanaugh, Director of Sales, Naveego
- Philip Rathle, V.P. Products, Neo4j
- Ariff Kassam, V.P. Products, NuoDB
- William Hardie, V.P. Oracle Database Product Management, Oracle
- Kate Duggan, Marketing Manager, Redgate Software Ltd.
- Syed Rasheed, Director Solutions Marketing Middleware Technologies, Red Hat
- John Hugg, Founding Engineer, VoltDB
- Milt Reder, V.P. of Engineering, Yet Analytics
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