Best practices for database management

When you are writing a script, PHP or just using a database, you may have to handle creating, updating and deleting records. A good relation between server and client is the key for a healthy project. This is why we are going to study some best practices of Database Management.

Before we get into the top best practices you should be following, lets take a look at how to manage databases, how to manage database and top 5 database management systems. A database contains potentially dozens — if not hundreds — of ads that all need to be managed and created. Managing your database is important because it will help make running it a lot more efficient. Being organized will help you respond to issues quickly and effectively, which helps you scale better.

Best practices for database management

Data management is a critical business driver used to ensure data is acquired, validated, stored, and protected in a standardized way. It is essential to develop and deploy the right processes so end users are confident their data is reliable, accessible, and up to date. To make sure that your data is managed most effectively and efficiently, here are seven best practices for your business to consider.

1. Build strong file naming and cataloging conventions

If you are going to utilize data, you have to be able to find it. You can’t measure it if you can’t manage it. Create a reporting or file system that is user- and future-friendly—descriptive, standardized file names that will be easy to find and file formats that allow users to search and discover data sets with long-term access in mind.

• To list dates, a standard format is YYYY-MM-DD or YYYYMMDD.
• To list times, it is best to use either a Unix timestamp or a standardized 24-hour notation, such as HH:MM:SS. If your company is national or even global, users can take note of where the information they are looking for is from and find it by time zone.

2. Carefully consider metadata for data sets

Essentially, metadata is descriptive information about the data you are using. It should contain information about the data’s content, structure, and permissions so it is discoverable for future use. If you don’t have this specific information that is searchable and allows for discoverability, you cannot depend on being able to use your data years down the line.

Catalog items such as:

• Data author
• What data this set contains
• Descriptions of fields
• When/Where the data was created
• Why this data was created and how

This information will then help you create and understand a data lineage as the data flows to tracking it from its origin to its destination. This is also helpful when mapping relevant data and documenting data relationships. Metadata that informs a secure data lineage is the first step to building a robust data governance process.

3. Data Storage

If you ever intend to be able to access the data you are creating, storage plans are an essential piece of your process. Find a plan that works for your business for all data backups and preservation methods. A solution that works for a huge enterprise might not be appropriate for a small project’s needs, so think critically about your requirements.

A variety of storage locations to consider:

• Desktops/laptops
• Networked drives
• External hard drives
• Optical storage
• Cloud storage
• Flash drives (while a simple method, remember that they do degrade over time and are easily lost or broken)

The 3-2-1 methodology

A simple, commonly used storage system is the 3-2-1 methodology. This methodology suggests the following strategic recommendations: 3: Store three copies of your data, 2: using two types of storage methods, 1: with one of them stored offsite. This method allows smart access and makes sure there is always a copy available in case one type or location is lost or destroyed, without being overly redundant or overly complicated.

4. Documentation

Within data management best practices, we can’t overlook documentation. It’s often smart to produce multiple levels of documentation that will provide full context to why the data exists and how it can be utilized.

Documentation levels:

• Project-level
• File-level
• Software used (include the version of the software so if future users are using a different version, they can work through the differences and software issues that might occur)
• Context (it is essential to give any context to the project, why it was created, if hypotheses were trying to be proved or disproved, etc.)

5. Commitment to data culture

A commitment to data culture includes making sure that your department or company’s leadership prioritizes data experimentation and analytics. This matters when leadership and strategy are needed and if budget or time is required to make sure that the proper training is conducted and received. Additionally, having executive sponsorship as well as lateral buy-in will enable stronger data collaboration across teams in your organization.

6. Data quality trust in security and privacy

Building a culture committed to data quality means a commitment to making a secure environment with strong privacy standards. Security matters when you are working to provide secure data for internal communications and strategy or working to build a relationship of trust with a client that you are protecting the privacy of their data and information. Your management processes must be in place to prove that your networks are secure and that your employees understand the critical nature of data privacy. In today’s digital market, data security has been identified as one of the most significant decision-making factors when companies and consumers are making their buying decisions. One data privacy breach is one too many. Plan accordingly.

7. Invest in quality data-management software

When considering these best practices together, it is recommended, if not required, that you invest in quality data-management software. Putting all the data you are creating into a manageable working business tool will help you find the information you need. Then you can create the right data sets and data-extract scheduling that works for your business needs. Data management software will work with both internal and external data assets and help configure your best governance plan. Tableau offers a Data Management Add-On that can help you create a robust analytics environment leveraging these best practices. Using a reliable software that helps you build, catalog, and govern your data will build trust in the quality of your data and can lead to the adoption of self-service analytics. Use these tools and best practices to bring your data management to the next level and build your analytics culture on managed, trusted, and secure data.

how to manage database

Your work isn’t done after a database is created. You need to have a number of good data management best practices in place in order to keep data quality high and database performance on target. 

Setting appropriate business goals for your database and helping your team support the database administrator allows your organization to get the most from your new (or existing) database. Learn how.

How database management has evolved

Earlier in the history of database management, administrators had a more hands-on role—they didn’t have today’s AI and automated support to rely on. In effect, this reality reduced the amount of data a DBA could manage and made the admin’s job a lot less strategic than it generally is now. 

Instead of having to endlessly manage databases and repair errors all day, the modern administrator is really becoming more of an architect—someone who is able to see the database’s opportunities and leverage them for the organization’s benefit. 

As you might imagine, this has opened exciting possibilities for data administrators and for the once “lowly” database. For example: 

• The role of AI: Automation does more of the heavy lifting now, empowering the database administrator. 
• Data at scale: Organizations are doing more with data, seeing the benefits of Big Data for next-generation applications. 
• Optimization: By unlocking the potential of databases, you can now optimize your database performance in new ways for speed, resource use, reliability, security, and more. 

Tips for modern database management

To help you get the most from your database, follow these database management best practices and data management tips: 

1. Set business goals

An actionable, targeted database management strategy should reflect your business needs and outline the metrics you’ll use to track your success. If you don’t spend enough time deciding what data to collect and how you can use this data effectively, you run the risk of wasting internal resources gathering the wrong data, piling up too much data to efficiently use, or missing important data opportunities. 

Setting relevant business goals gives you a lodestar to follow so you don’t lose your way. These uses for business data are worthwhile to consider: 

• Creating profiles and targeting: Customer profiles are a common way to use data collection and analysis, but the user, partner, and audience data you are collecting could also be valuable. Forming profiles from accumulated data is a smart way to start making sense of it.
• Identifying trends and patterns: Customer trends, sales trends, and other patterns provide you with strategic insight into your industry and the purchasing behaviors of those who patronize your products and services. Usage patterns, consumption trends, and other conditions can be tracked, and this information is particularly valuable for SaaS companies and other organizations that could strategically benefit from understanding trends. 
• Automating and improving processes: You can also use data to help you revamp your processes, implement automation, and make adjustments. Looking closely at your data may reveal opportunity areas. 
• Informing business decisions: The next best thing to a fortune-telling crystal ball is the ability to dial in on your past experiences and collected data. 

With a close eye on your data, database administrators are in a good position to help with sorting through these data uses and determining which other opportunities exist for your organization. 

2. Establish policies and procedures, including backup and recovery procedures

Crafting specific backup and recovery procedures and policies prepares your team to act more effectively if the worst happens. Determine smart actions you can plan in advance—this exercise will keep the team focused and give you a chance to work through your worst-case scenarios. 

As you plan your disaster response, you can use flowcharts and process mapping to visualize everything and provide your team with a helpful overview. 

• Collecting and organizing data: Your team should be trained on your procedures and policies for collecting data and adding it to the database. With the role automation now plays in this, make sure everyone is on the same page with how software contributes and what role AI and automation plays.
• Guard data integrity: Database errors can be devastating, so make a plan for keeping them minimal and allowing your team to find and correct them when they occur. 
• Monitor your data: On a regular basis, check your databases for accuracy and possible data corruption. 
• Set benchmarks: Your DBA should help with establishing alerts to protect the database by bringing up problems when they occur. Your team should know what your organization’s goals are for the database and be prepared to act accordingly if changes happen. 
• Map your processes: Being able to visualize how your database works and see the entire process, from data collection to processing, assists your organization with troubleshooting and planning. 

3. Make security your priority

Although not every disaster is entirely predictable or preventable, you can improve your data security and manage the risks associated with worst-case scenarios for your database. Maintenance, backup, and recovery planning are your best bets for protecting what’s important. 

DBAs who know industry best practices for database security and are prepared to manage your database security effectively are valuable allies in the fight against data loss, security breaches, and database compromise. 

• Create a comprehensive maintenance plan: Maintain your database regularly. Data security should stay at the forefront and not become an afterthought. You don’t want to be in the position of trying to “catch up” on security after a breach, for instance. Make a plan your team can use as a preventative treatment—it’s easier than a cure. 
• Develop your backup and recovery procedures: Have your backup and recovery plan together and review it to make sure it still fits your security strategy, your team, and your database. 
• Build your team’s security skills: Security issues change along with technology changes, business growth, and database characteristics. Your team should stay up to date with the industry and strive to stay ahead of your database’s needs. 
• Leverage automation to help with security: Automation can support your DBAs, too—for example, you can schedule frequent automated backups. 

4. Focus on the quality of the data

Your DBA should work to promote a high standard of data quality, removing data that doesn’t meet the standards and adapting quality standards to fit your changing strategy.

• Have SMART data quality metrics: Ideally, you would create metrics that meet SMART (specific, measurable, achievable, relevant, and time-bound) standards. SMART helps you make data quality metrics that are usable and truly useful for your organization. At best, metrics that don’t fit SMART criteria represent “nice to have” goals that are subjective. At worst, these metrics leave your team aiming for moving targets. 
• Empower your data steward: As your DBA goes about their job protecting your data quality, make sure they have everything necessary to do their job successfully. Loop them into communications with the rest of your team, allow them to enforce your data quality standards, and make sure organizational resources are available to help them protect your data. The last thing you want is a situation where the data czar doesn’t have management or team support.

5. Reduce duplicate data

Duplicate data reduces your database performance and can hinder your efforts. Often, duplicates also lead to wasted internal resources and doubled effort by your team. If a customer record is duplicated in a CRM, for instance, the service team might literally spend twice as much time fixing the same problem all over again. 

• Share data quality basics throughout the organization: Your entire company should know a few basics about protecting data quality, even if they don’t work directly with the DBA or with the database. Someone who doesn’t know the harm of creating duplicate records can create more work for your team. Teach everyone what good data looks like and how to contribute high-quality data. 
• Eliminate siloed data access and management: When individual departments manage separate areas of a database or manage their own without any outside direction or input within your organization, you risk duplication and errors. In some cases, these silo databases exist without following data quality and duplication standards. 
• Have a plan for duplicate data and test your database: If duplication seems to happen more often to your organization than you want it to, then develop a plan to address the sources of duplication. Test your database regularly to make sure you’re managing the problem effectively with these changes in place. 

6. Make the data easily accessible

You need to make sure that your users can benefit from the data. Internal users, end-users, and other stakeholders that access your database should know how to use it and be comfortably able to benefit from it. 

• Design and manage for the user: Think of how your database is used and design it accordingly. Keep in mind that some shortcuts or development and management strategies might work well for your team but be poor choices from a UX/usability or performance perspective. 
• Gather feedback on your database: Your users should be able to provide feedback on how the database is working for them. How you gather this feedback is up to you and depends on the stakeholders you have—choosing a survey, forming a panel or committee meeting, or designating a DBA as a point of contact are potential approaches. 

Database management best practices enable DBAs to maintain databases more effectively. With today’s increasingly complex data management and multi-cloud environments, the DBA’s role needs the right resources and support from your team to guard data and keep your database healthy. 

top 5 database management systems

1 Improvado

Improvado is a revolutionary DBMS software for revenue data containing both database and ETL functionalities under one hood. The platform aggregates marketing and sales insights from 300+ data sources in centralized storage. 

Data gathered from disparate platforms must be unified and aligned. Improvado provides automated data cleansing and transformation functionalities, ensuring the highest quality of insights for future analysis. 

With Improvado, marketing and sales teams don’t have to be stuck with one particular warehousing solution. The platform provides access to managed BigQuery, Snowflake, or Clickhouse DBMS.

The crucial point for marketers is that they don’t need technical expertise and human resources to manage DBMS. Teams receive a turn-key solution where they can work in a zero-code, spreadsheet-like UI. Besides, Improvado is more cost-efficient and optimized for complex data transformations than popular data warehouse solutions.

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Pros

• Automated data cleaning, deduplication, and transformation processes
• Quick integration and data extraction from 300+ data sources 
• Efficient Clickhouse-based storage
• Integration with 10+ visualization tools to build real-time marketing dashboards
• Analysis-ready data without any manual effort

Cons

• The data often reverses the newest entries amidst analytical comparison.

Cost Structure

Improvado’s pricing is tailored to clients’ needs and business objectives. The final price depends on the number of data sources you’re going to work with and the additional features you might need. Get in touch with Improvado’s analytics experts to watch a product demo and get an estimate custom-built for your specific use case.

Optimize your marketing and sales data processes with Improvado

2 Microsoft SQL Server

Microsoft’s SQL Server is one of the most effective DBMS in existence. The free tag of the tool certainly attracts a large user base. Its custom-built graphical integration of best database designs has saved users’ valuable time for years. Similarly, the diagrams that you can make with the help of this tool can be easily added to a new or existing project library.

Object Explorer feature helps end-users to view the creation of the tables. Template Explorer, on the other hand, is a bundle of script objects that can be used to identify numerous names in the database system. In addition, the SQL Server creates specific containers that allow users to combine views and scripts of a linked object or group.

Pros

• Easy to set up a new database server from scratch
• Creates various designs, tables, and view data without syntax
• Can handle complicated queries and integrate with other programs
• Creates advanced queries through Developer Network feature

Cons

• The lightweight package lacks user-friendliness in its user-interface.
• The execution of long queries often takes longer than the calculated time.
• Data maintenance becomes an issue with a selective schema.
• Desperately needs a new firewall protection system.

Cost Structure

The Express and Developer versions are free and ideal for personal use. 2016, however, is the most popular version priced at $931 for a Standard License. The 2017 and 2019 versions have the same price tag. 

3 Postgre SQL

The open-source DBMS solution gets attention because of the invigorating indexing and configuration options. Postgre SQL is ideal if your daily business activities require you to import or export data.  

As of now, Postgre SQL supports Python and JSON programming languages. Although it is a relational database solution, users are free to create NoSQL databases. Besides, the open-source community has created a wide array of plug-ins to boost the functionality of the software.

Pros

• Storage and management of data in higher volumes 
• Relatively secured data processing than others
• Straightforward installation process on Linux and Windows operating system (OS)
• Availability of resourceful material such as tutorials to learn the tool
• Ideal for companies that frequently deal in large volumes of data

Cons

• Native interface limits the manipulation of data.
• The advanced nature of the tool slows down the insertion of small databases.
• The installation and configuration of the software can be time-consuming.

Cost Structure

PostgreSQL is available free of cost.  The distribution license of the tool allows users to resell binaries, thanks to open source.

4 My SQL

My SQL is a high-speed data processing and data productivity tool with comprehensive features. The tool is designed to increase the security and scalability of your databases. A reliably cost-effective tool offers technical support and counteracts potential risks. Furthermore, high-volume business sites can deploy business-oriented complex MySQL applications.

Pros

• The newest 8.0 version has better data recovery options
• Easy to learn the foundational features without a programming background
• The open-source nature grant users complete freedom to customize data
• Well-suited for small businesses and entrepreneurs because of the low-cost structure
• Compatible with up-to-date industry’s DBMS practices
• Instant integration with Apache web engines
• The data development process is adjustable for small and heavy applications.

Cons

• Queries get stuck even after a refresh or restart
• There is overdependence on third-party add-ons
• Data operations in Linux OS can get complicated.

Cost Structure

MySQL Standard, Enterprise, and Cluster Carrier Grade editions are set at $2,000, $5,000, and $10,000 respectively.

5 Amazon RDS

Amazon RDS (Relational Database Service) is one of the best DBMS tools.  It has a dedicated secured connection, and it automatically backs up your data through an inbuilt feature. Furthermore, it can resize your entire database activities.

Pros

• Users can process the heavy workloads in a single database.
• You have to pay for the used resources.
• It can get you access to MySQL, Oracle, or Microsoft SQL databases. 
• Point-in-recovery attracts programmers who want flexibility and scalable storage options. 

Cons

• It has limited auto-scale options.
• Unavailability of access to physical server to check server logs.

Cost Structure

Users are free to try out Amazon RDS. In fact, there’s no minimum criterion to use the tool. You can pay for the availed resources via On-Demand method. It may sound complicated, but you can instantly calculate your monthly bill with the AWS Simple Monthly Calculator.