To learn about the current and future state of databases, we spoke with and received insights from 19 IT professionals. We asked, "What are the most common issues you see companies having with databases?" Here’s what they shared with us:
Scalability
- Scalability is a common issue. We need to design the database and IT stack to cope with more data. Data volumes are only going up. Accessibility of the data and usability. Data is not very valuable unless you are gaining insight from it. Make sure you are deriving useful information from it.
- The number one issue is around scale, performance, and cost efficiency. The second is vendor lock-in. The third is architectural, as customers make choices around more cost-effective solutions and build databases on top of shaky foundations, they have to go back and look at a common provider.
- 1) A large proportion of our customer base used to struggle to meet their SLAs because their platforms couldn’t deliver high transactional throughput and low latency, at scale. For them, this resulted in business losses, reputations being compromised, and general dissatisfaction all around. They needed very high performance and low latency without having server sprawl and exponential cost, which is why they came to us. 2) As the database market evolves, companies are finding it difficult to evaluate and choose a solution. There are relational databases, columnar databases, object-oriented databases, and NoSQL databases. Sometimes, businesses are not able to differentiate between them. That’s something that we (as a software industry) needs to improve on. 3) Legacy systems still cause customers problems. This is due to skills shortages, risk integrating and updating old platforms with newer technology and sometimes frayed relationships between the business and IT, based on historical politics. 4) IOT, sensors, social media et al. have contributed to data explosion and companies are struggling to cope. Research shows that we’ve created more data in the past two years than in the entirety of the human race. 5) There are considerable benefits to decentralized data management, but it presents challenges as well. How will the data be distributed? What’s the best decentralization method? What’s the proper degree of decentralization? A significant challenge in designing and managing a distributed database results from the inherent lack of centralized knowledge of the entire database.
- 1) Latency spikes with mixed workloads at scale. Cassandra handles ingest, but if you do with it read and analytics, it spikes. Ingestion is becoming real-time it’s not batch anymore. Almost all solutions have latency spikes. Look at benchmarks. 2) Scale — systems have limits, or it becomes impossible to manage at scale because it’s so complicated. The need for manual scaling and scale limits at petabytes and billions of requests is a big problem. 3) Data loss — can I trust the new transactional data solutions to take care of my data? 4) Silos — multiple apps on non-integrated applications without spinning up on different database instances. 5) Security — mission-critical systems of record ideas if you don’t have security all of the time is a non-starter.
