Database Management and Administration Developer with NoSQL Salary in 2024
How statistics are calculated
The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.
Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.
Trending Database Management and Administration tech & tools in 2024
Database Management and Administration
What Is Database Management?
A database management system (DBMS) is a software tool that controls the organization, storage, and retrieval of data from a database. It contains various functions that are interdependent to ensure data integrity for data entry and management with respect to recording, retrieving, and presenting data.
A database management system consists of three main elements:
- A physical database that contains the data.
- A database engine that helps to access the data and modify its contents.
- A relational database scheme (logical design of data in the database).
The ‘physical design of data’ consists of complex tables. While this does provide useful information at a technical level, it doesn’t provide important diagnostic information regarding the main question at hand.
Types of Database Management Systems
Centralized Database Management System
It would all be there. It would all be there in one piece of kit, in one building. If you wanted access to the data, you’d need to come to that system.
Distributed Database Management System
Thanks to this construction, it becomes possible to locate different pieces of data on different nodes so that the absence of each individual node does not prevent us from accessing the data.
In comparison with centralized database systems, a distributed DBMS system makes a number of complete copies of the same data, or at least parts of it, and therefore, in case one of the systems fails, the necessary information is not lost.
Federated Database Management System
A federated database system maps several autonomous databases into one giant database system. Data need not be duplicated, as happens in a distributed DBMS, and hence is very appropriate to handle a distributed integration project.
Federated databases are further divided into two types:
- Loosely Coupled: Each component database constructs its own federated schema.
- Tightly Coupled: The component systems use independent processes to instantiate and publish a unitary federated schema.
Blockchain Database Management System
A blockchain is also called a database. This is because this type of technology stores transactions in data structures, known as blocks. The blockchain system has been widely used to save financial and other types of transactions.
Cloud Database Management System
A cloud database management system is a distributed database providing Cloud-as-a-Service (CaaS) on which the resources, software, information, and other data are shared through a network. The cloud is the main storage that people can access the data across an entire planet.
Importance of Data Management
Data have become one of the core values of companies. It could help a business to make decisions, enhance marketing campaigns, and perfect operational processes. Nevertheless, without the right database management system, companies may face data silos, different data sets, and poor data, which could lead to poor findings.
Businesses always have a reason behind database management. Here are some of the common and significant reasons for database management:
- To run business operations
- To automate processes
- To ensure adequate performance of queries
- To keep up with consumer demands
- To organize and track customers, inventory, and employees
- To store different types of data
Further, another inherent facet is the need for database management to abide by the ever-growing compliance rules of data protection acts, including GDPR, CCPA, and so forth.
Benefits of Database Management
There are a number of benefits that database management systems provide to businesses.
Self-describing Nature
A DBMS is self-descriptive, because it will contain both data and metadata about the database. This self-describing characteristic distinguishes a DBMS completely from any other file-based system.
Supports Multiple Views of Data
A database system can have many views, one for each user of the system. Each view should contain only the data of interest to one user or a group of users.
Multi-user System
Users accessing these concurrently can all contact the same database at a later time. This is possible because there are several techniques to ensure that the data is always correct and data integrity is maintained. This is referred to as concurrency control.
Restriction of Unauthorized Access
It varies accordingly from user to user. Some users might have read-only access while some can have read-write access using this system, which results in internal data control by limited access and prevents unauthorized access by other individuals.
Database Administrator
An average database administrator has to work with many tasks daily. Mainly, there are some fundamental tasks of database administrators:
- Troubleshooting database issues.
- Backing up and restoring databases.
- Performing scheduled maintenance tasks.
- Installing new systems.
- Writing SQL scripts.
Updating Databases
Maintaining the databases is the most essential part of the work of a DBA. A database that is amended and updated regularly is the one that is taken into use. A DBA maintains the database by renewing and issuing the permissions for database access. A DBA also takes care of all the changes in employment, such as changing shifts or roles and responsibilities. Similarly, the software in a database is updated regularly by DBAs. In software updates, the code needs to be written and stored. The database systems and programming languages associated with the database also undergo regular maintenance and need to keep up with the updates.
Organizing and Maintaining Data
A DBA needs to do this filtering and prepping of data – such as financial records, customer data, purchase histories, and so on. An application DBA uses specialized software to be able to put data in good order. How does the data get organized, especially without us knowing what we will want to do with it in the future? That’s one of the ongoing efforts of the DBA. They need to maintain that data, or have it in an order such that the person who is going to mine this data would find it relatively easy to be able to do the mining, to identify some key insights, some trends, some key performance indicators (KPIs), and metrics from the data, so that such data becomes very important in making decisions that are data-driven.
Designing and Developing Databases
Database design and implementation also involve how we organize data. Although DBAs might not be data engineers, system administrators, or application programmers, they can spin up brand new databases from scratch and then make sure those databases behave. DBAs can also help by putting themselves in the shoes of database users. If one can think from a database user’s perspective, this can lead to robust data models that perform the necessary tasks. By enabling supporting applications and infrastructure, one can assemble databases.
Archiving Data
The database administrator is by trade a data archivist. He is a seeker of useless data and is the human who will remove said unnecessary data. If the database administrator deems no one is using it, the data will be archived. Any unnecessary data that is used less frequently or that is kept for legal or industry reasons will be archived once deemed necessary by the database administrator. In this way, the data is kept out of the operational production system. Archiving data of less frequent use increases the active operational capacity, allows for a more efficient operation and a data backup, and ensures the DBMS is kept available for active operation.
Maintaining and Troubleshooting Databases
Maintenance is not the totality of DBA work; DBA maintenance includes database maintenance. DBAs don’t simply maintain data, they also maintain databases. Maintenance of any kind entails making tests and making adjustments while monitoring what a given entity (‘program’, ‘car’) is doing. That is why a DBA who knows several forms of database management systems such as Oracle or SQL (MySQL, SQL Server, DB2, Oracle, etc.) can maintain and troubleshoot them, not to mention an essential database maintenance know-how in SQL, the Structured Query Language. DBA maintenance also includes installation, maintenance, and updates for database management systems.
Database backups need to be done, effective disk use and application being hung up or even transaction rollbacks need to be monitored, and applications cannot be allowed to hang. In addition, the physical implementation of the database needs to be monitored and DBAs need to be aware of what is happening to the database with respect to performance so that they can adjust the database if it is performing poorly.
Improving Data Security
One of the major responsibilities of a knowledgeable DBA is to improve the security of the data through access control. The database administrator provides access to the users who need access to the data and removes a user’s access to any information, also altering a user’s access and privileges.
Other security responsibilities of a database administrator include:
- Providing data security education to database users.
- Creating best practices for data security.
- Collaborating with cybersecurity specialists on data security initiatives.
Database administrators also work to optimize securities by learning the latest in technological advances that prioritize security, as well as many of them obtaining cybersecurity certifications, which place them as more competitive job candidates and as more knowledgeable DBAs.
Creating Database Documentation
What some of a DBA’s duties are includes, among others, helping users, who are not experts in using databases, to use a system securely. Developing database documentation can be helpful to database administrators to prepare for this.
Documentation defines the database and its data contained in it and describes how the database is used and the applications it supports. Documentation defines how users who are authorized can be properly identified, how the system can be properly restored or restarted from a backup, and how authorized users can succeed in accessing the database. Documentation defines how cataloging of the data can be done. The data catalog lists what data is contained in the database, who the database owners are, and how the data can be accessed.
Training Others in Database Management
Employees of a DBA’s team need to take an overview of database management in the company. A database administrator may teach database availability, inputting data, database concepts, queries by SQL in a database, and reports generated from the database. Sometimes DBA documents about the training for members of the team.
Performing Backup and Recovery
Everyone who works with a database knows that database backups are a key requirement. Everyone regularly backs up the database and takes steps to ensure that those backups can be restored. DBAs continue to test and certify those backups regularly to ensure they continue to work as expected.
Planning for Future Growth
For database administrators, mitigation involves planning for the growth of database systems. To do so effectively, the DBA first looks forward in time and examines the usage patterns of the database system and its overall capacity needs. The DBA then, focusing on trends, suggests what type of system changes and upgrades might become necessary.
Planning an IT career? Learn about a database administrator’s typical work activities, skills, and pay in Your Next Move: Database Administrator.
Skills Required for a Career in Database Administration
A great database administrator has a very solid toolkit, enough to embrace all of the roles listed above, and more. A database administrator should have skills in:
- Data analysis
- HTML
- Database management
- Database providers
- Troubleshooting
- Database design
- Database structure
- Programming languages
- Information systems
- Data management
- Performance tuning
- Data architecture
- Database security
- Indexes
- End user management
- Data science
- Data administration
- Data analytics
A DBA should be knowledgeable working with Linux, UNIX, and Windows Operating System and Microsoft Access, Microsoft SQL Server, MySQL, Oracle, and other database and computer systems and operating systems. By and large, most DBAs work well within an organization, have great communication skills, and are organized and logical in their thinking.
Those skills can be acquired through experience in the performance of database-specific tasks, and can be augmented by independent study and the best database certifications. Decreasingly in trend, DBAs achieve a bachelor’s degree in either computer science, information science, or management information systems. Some possess a master’s degree in one of the same.
Where is NoSQL used?
Feeding Time at the Social Media Zoo
- These digital jungles use NoSQL to keep millions of critters—ahem, user profiles—munching through feeds without a hiccup.
Gaming Gardens Grow with NoSQL
- Gamers plant seeds of victory in online arenas where NoSQL fertilizes real-time scores and beat-the-clock data sprouts.
E-commerce Vines Twisting through NoSQL
- NoSQL is the trellis for climbing e-commerce vines, ensuring that shopping carts don't tumble when customers go grape-crazy on sales.
The Library of Babble: Cataloging with NoSQL
- In the world’s most hush-hush library, NoSQL is the silent librarian categorizing books and user queries faster than a shush!
NoSQL Alternatives
SQL Databases
Structured Query Language (SQL) databases are relational, table-based systems designed for complex queries with ACID compliance. Examples: MySQL, PostgreSQL.
-- Example SQL query
SELECT * FROM users WHERE age > 20;
- Strong consistency model
- Complex query capabilities with JOIN operations
- Mature with extensive tooling
- Can be less scalable for write-heavy applications
- Rigid schema can impede agility in rapidly evolving datasets
- May underperform in big data scenarios
In-Memory Data Stores
In-memory data stores offer rapid data access by holding data in RAM. They are used for caching and real-time analytics. Examples: Redis, Memcached.
-- Example Redis command
SET user:1000 "John Doe"
- Extremely fast read/write operations
- Flexible data structures
- Great for ephemeral data and caching
- Data may be lost on power failure if not persisted
- Limited storage capacity tied to RAM size
- Often used alongside other DBs due to volatility
NewSQL Databases
NewSQL databases aim to provide the scalability of NoSQL while maintaining the ACID guarantees of traditional SQL databases. Examples: Google Spanner, CockroachDB.
-- Example NewSQL syntax (CockroachDB)
BEGIN;
UPDATE accounts SET balance = balance - 100 WHERE id = 1;
UPDATE accounts SET balance = balance + 100 WHERE id = 2;
COMMIT;
- Scalability with consistency
- Advanced features for distributed computing
- SQL interface for relational data handling
- Might be more complex to administer
- Can be cost-prohibitive for small-scale applications
- Newer, with less established community
Quick Facts about NoSQL
NoSQL Databases: The Non-Conformists of Data Storage
Once upon a not-so-distant past in the year 1998, Johan Oskarsson flicked a switch in the world of databases when he organized an event named after Carlo Strozzi’s brainchild, the 'NoSQL' database. NoSQL cheekily stood as the antithesis to SQL's structured query language strictness, waving goodbye to rigid schemas and embracing flexibility like a hippie loves tie-dye.
Viva la Evolución: The Rise of Schema-less Storage!
Faster than a speeding query, NoSQL databases like MongoDB, released in 2009, championed the document model, yanking away the relational database’s necessity for predefined schemas. This was akin to a software whisperer gently saying to the data, "Be free, my structured friends!" and oh, how they danced. They also made friends with JSON and BSON, two formats that help them play nice with web applications.
{
"firstName": "Zero",
"lastName": "Schemas",
"contact": {
"email": "free@data.com",
"phone": "123-456-7890"
}
}MongoDB and the Curious Case of Version 5.0
Leaping through time to 2021, MongoDB introduced its version 5.0 with Live Resharding, which is basically the database's way of saying, "You know what? I can change my mind whenever I feel like it." This allows admins to rejig the data’s distribution across a cluster without downtime—a feat similar to changing the tires on a moving car while blindfolded. Also, it acquired some time series collection abilities, for all those who like their databases with a side of foretelling the future.
db.runCommand( { reshards: 'user_data', key: { userId: 1 } } )What is the difference between Junior, Middle, Senior and Expert NoSQL developer?
| Seniority Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
|---|---|---|---|
| Junior Developer | 0-2 years | $50,000 - $80,000 |
|
| Middle Developer | 2-5 years | $80,000 - $110,000 |
|
| Senior Developer | 5+ years | $110,000 - $150,000 |
|
| Expert/Team Lead | 8+ years | $150,000 - $200,000+ |
|
Top 10 NoSQL Related Tech
JavaScript (of The NoSQL Persuasion)
Picture this: you're frolicking in the NoSQL meadow, and there stands JavaScript, the unicorn of programming languages. Omnipresent in web development, it prances its way into NoSQL land with its trusty companions Node.js and JSON. Why? Because when you're dealing with unstructured data, you need a language that doesn't raise its eyebrow at a little chaos. JavaScript says, "Array of objects as a database? Pfft. Watch me." And indeed, we do.
db.superheroes.find({ capeColor: "red" })
Node.js (Server-side Shenanigans)
Ah, Node.js, where JavaScript sheds its browser-only cape and takes on server-side quests! With this event-driven knight, you'll trot through I/O operations without ever breaking into a gallop, because who has the time for that? Plus, npm—with its treasure trove of packages—lets you arm yourself with tools like Mongoose. Getting cozy with MongoDB was never more straightforward. Mmm, the smell of asynchronous code in the morning!
npm install mongoose --save
MongoDB (The Document Store Darling)
Enter MongoDB, the hipster of databases, snubbing rows and columns for documents and collections since 2009. Why stifle your data in rigid schemas when it can be as free as a JSON-like BSON document? Here, your agile data dons a leather jacket and hops onto a schema-less motorcycle, ready to zip and zoom without a care for structure. And with operators like $match and $group, aggregations are slicker than a greased handlebar mustache.
db.inventory.aggregate([ { $group: { _id: "$item", total: { $sum: "$qty" } } } ])
Redis (The Speedy Keystore Wizard)
Watch out! Redis is zipping by, casting spells of sub-millisecond response times with its enchanting in-memory data structure store. Most known for its cache magic, Redis plays peekaboo with data structures like strings and hashes that virtually whisper, "Seeya SQL, hello speed!" And let's not forget about Pub/Sub, where messages are passed around like notes in magic school—fast and reliable.
SET superpower "invisibility"
GET superpower
Cassandra (The High-availability Herald)
Cassandra strides in, her head held high, boasting availability and fault-tolerance fit for battle. She crafts a tapestry of decentralized, peer-to-peer nodes that scoffs at single points of failure. With her, your data sets sail across a Viking fleet of servers, partitioned and replicated like plunder. CQL might echo SQL's song, but make no mistake, it dances to a different tune—a bolder, wider-reaching choreography.
SELECT * FROM vikings WHERE name = 'Ragnar';
Apache HBase (The Bigtable Benefactor)
Oh, the grand Apache HBase, inspired by Google's Bigtable and part of the Hadoop family! Imagine a banquet hall of never-ending tables with rows stretching to the horizon. This is the realm of sparse, distributed, multidimensional maps indexed by a rowkey. Wield Column Families like a royal scepter, and partition your kingdom's data with preordained reverence. Big Data, meet your roomy abode.
scan 'epicTales'
Couchbase (The Cluster Conductor)
The maestro of seamless scaling, Couchbase, conducts a symphony where every node is first chair. Here's a document database that strolls nonchalantly through both cache and store, keeping JSON-documents at the heart of a meticulously replicated and sharded cluster. Tame these wild clusters with N1QL, and dock RESTful APIs like ships at its versatile ports. The crescendo? A performance that scales elastically to rapturous applause.
SELECT airportname, city, country FROM `travel-sample` WHERE type="airport" AND city="London";
Elasticsearch (The Full-text Search Sorcerer)
Need to conjure search results faster than you can say "abracadabra"? Elasticsearch enters with a twirl of his wand—the revered full-text search engine, tailored for horizontal scalability, reliability, and a real knack for real-time indexing. Juggling JSON documents with ease, this wizard enchants with RESTful spells and search queries that can pinpoint a needle in a data haystack. It's like speed dating...with your data!
GET /wizards/_search?q=spell:fireball
DynamoDB (The Serverless Charm Caster)
DynamoDB, Amazon's brainchild, is the serverless treasure for those who abhor server maintenance as much as Voldemort abhors happiness. Auto-scaling, built-in redundancy, and a performance that laughs in the face of peak traffic—expect it all, as if granted by an AWS genie. Got a batch of data? Wave your provisioned throughput wand and watch it write and read in perfectly predictable performance puffs. Scaly clusters beware!
Scan TableName="ForbiddenSpells";
Neo4j (The Graph Database Guru)
Neo4j, the sensei of connected data, assembles your intertwined web of nods and edges like an origami master. Grasp the deep relationships within your data, traversing paths and nodes through graph algorithms that weave tales of connections mere mortal databases can't discern. It's philosophy class meets database—where questions like "Who are you connected to?" and "How many degrees separate you?" find artful answers.
MATCH (n:Person)-[r:LIKES]->(m:Technology) RETURN n, r, m