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CTRL+ALT+DELETE: 5 Tips for Avoiding Data Disasters

Yaniv Yehuda

It's every system administrator's worse nightmare. An attempt to restore a database results in empty files, and there is no way to get the data back, ever.

Despite the fear and panic created by data loss, more often than not it's due to simple things that are under our control and can be prevented. Studies have shown that the single largest cause for data outages is human error. Regardless of how much you try, there are still going to be mistakes and you have to account for them in the way database changes are managed.

Here are five simple tips for keeping things running smoothly and minimizing risk.

1. Define roles and responsibilities

Safeguards need to be put in place to ensure that only authorized people have access to the production database.

The level of access shouldn't be determined only by an employee's position but also by the level of seniority. A famous story made the rounds last year when a developer shared that while following instructions in a new employee manual, he accidentally deleted the production database. To make things worse, the backup was 6 hours old and took all too long to locate. You might be shaking your head in disapproval right now over how the company could have been so irresponsible to let this happen, but it turns out … it's really not uncommon (check out the comments on this tweet).

To prevent unauthorized changes in the database that can result in utter disaster, it is essential to define, assign, and enforce distinct roles for all employees. If you need to, set roles and permissions per project to avoid any accidental spillover.

2. Confirm back up procedures

You need a well-planned backup strategy to protect databases against data loss caused by different types of hardware, software, and human errors.

You'd be surprised by how often backups simply aren't happening. In one case a sys admin complained that bringing hard drives home with backed up data was inconvenient, so the company invested in an expensive remote system; the same sys admin never got around to creating the new procedure, so the latest version of the backed-up data was 3 months old.

Another employee discovered at his new job there hadn't been a single back-up for the past three years.

Knowing the back-ups are happening isn't enough. You also need to also check to make sure they are usable and include all the data that's needed. It's worth restoring and then checking that the restored database is an exact match to the production data. A check such as "Is the most recent backup size within x bytes of the previous one" is a simple solution to make sure the restored database matches the production database.

3. Adopt version control best practices

Version control practices have long since been adopted in other code development environments, ensuring the integrity of code as only one person can work on a segment at any given time.

Version control provides the ability to identify which changes have been made, when, and by whom. It protects the integrity of the database by labeling each piece of code, so a history of changes can be kept and developers can revert to a previous version.

Bringing these practices into the database is crucial for data loss prevention, especially in today's high-paced environment with increasingly shorter product release cycles. By tracking database changes across all development groups you are facilitating seamless collaboration, while enabling DevOps teams to build and ship better products faster.

4. Implement change policies

Databases are code repositories, so they need the same safeguards when changes are made. It's crucial to have clear policies on which changes are allowed and how they are administered and tracked.

Is the action of dropping an index in a database allowed? How about a table? Do you prohibit production database deployments during daytime hours? All of these policies should not only be practiced by participating teams, but enforced on the database level, too. Keep track of all the changes and attempted changes made. A detailed audit can help detect problems and potential security issues.

5. Automate releases

By taking advantage of comprehensive automated tools, DBAs and developers can move versions effortlessly from one environment to the next. Database development solutions allow DBAs to implement consistent, repeatable processes while becoming more agile to keep pace with fast changing business environments.

Automation also enables DBAs to focus instead on the broader activities that require human input and can deliver value to the business, such as database design, capacity planning, performance monitoring and problem resolution.

Databases often hold the backbone of an organization, a priceless container for the transactions, customers, employee info and financial data of both the company and its customers. All this information needs to be protected by following clear procedures for managing database changes. Reducing the likelihood of data loss due to human error can help everyone sleep better at night.

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CTRL+ALT+DELETE: 5 Tips for Avoiding Data Disasters

Yaniv Yehuda

It's every system administrator's worse nightmare. An attempt to restore a database results in empty files, and there is no way to get the data back, ever.

Despite the fear and panic created by data loss, more often than not it's due to simple things that are under our control and can be prevented. Studies have shown that the single largest cause for data outages is human error. Regardless of how much you try, there are still going to be mistakes and you have to account for them in the way database changes are managed.

Here are five simple tips for keeping things running smoothly and minimizing risk.

1. Define roles and responsibilities

Safeguards need to be put in place to ensure that only authorized people have access to the production database.

The level of access shouldn't be determined only by an employee's position but also by the level of seniority. A famous story made the rounds last year when a developer shared that while following instructions in a new employee manual, he accidentally deleted the production database. To make things worse, the backup was 6 hours old and took all too long to locate. You might be shaking your head in disapproval right now over how the company could have been so irresponsible to let this happen, but it turns out … it's really not uncommon (check out the comments on this tweet).

To prevent unauthorized changes in the database that can result in utter disaster, it is essential to define, assign, and enforce distinct roles for all employees. If you need to, set roles and permissions per project to avoid any accidental spillover.

2. Confirm back up procedures

You need a well-planned backup strategy to protect databases against data loss caused by different types of hardware, software, and human errors.

You'd be surprised by how often backups simply aren't happening. In one case a sys admin complained that bringing hard drives home with backed up data was inconvenient, so the company invested in an expensive remote system; the same sys admin never got around to creating the new procedure, so the latest version of the backed-up data was 3 months old.

Another employee discovered at his new job there hadn't been a single back-up for the past three years.

Knowing the back-ups are happening isn't enough. You also need to also check to make sure they are usable and include all the data that's needed. It's worth restoring and then checking that the restored database is an exact match to the production data. A check such as "Is the most recent backup size within x bytes of the previous one" is a simple solution to make sure the restored database matches the production database.

3. Adopt version control best practices

Version control practices have long since been adopted in other code development environments, ensuring the integrity of code as only one person can work on a segment at any given time.

Version control provides the ability to identify which changes have been made, when, and by whom. It protects the integrity of the database by labeling each piece of code, so a history of changes can be kept and developers can revert to a previous version.

Bringing these practices into the database is crucial for data loss prevention, especially in today's high-paced environment with increasingly shorter product release cycles. By tracking database changes across all development groups you are facilitating seamless collaboration, while enabling DevOps teams to build and ship better products faster.

4. Implement change policies

Databases are code repositories, so they need the same safeguards when changes are made. It's crucial to have clear policies on which changes are allowed and how they are administered and tracked.

Is the action of dropping an index in a database allowed? How about a table? Do you prohibit production database deployments during daytime hours? All of these policies should not only be practiced by participating teams, but enforced on the database level, too. Keep track of all the changes and attempted changes made. A detailed audit can help detect problems and potential security issues.

5. Automate releases

By taking advantage of comprehensive automated tools, DBAs and developers can move versions effortlessly from one environment to the next. Database development solutions allow DBAs to implement consistent, repeatable processes while becoming more agile to keep pace with fast changing business environments.

Automation also enables DBAs to focus instead on the broader activities that require human input and can deliver value to the business, such as database design, capacity planning, performance monitoring and problem resolution.

Databases often hold the backbone of an organization, a priceless container for the transactions, customers, employee info and financial data of both the company and its customers. All this information needs to be protected by following clear procedures for managing database changes. Reducing the likelihood of data loss due to human error can help everyone sleep better at night.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...