The world of data has morphed considerably, with Big Data, Visualizations, Self Service ETL, NoSQL databases, Graph, Machine Learning, Quantum, Artificial Intelligence as well as Virtual Reality, 3D Printing, Gaming, Internet of Things, BlockChain.
No more static reporting with single query database embedded within the reporting GUI.
It seems data now requires knowledge of this, a bit of that, some of this, and that.
We've had table creation, and views, and stored procedures, mostly within the database GUI.
That's a given now. Add in more layers. Connectors to other things. Security. Permissions. Gateways. Building out solutions in stand alone components, that interact with other components.
The hardware is less concern now, but spinning up correct version of service is now a must have, licencing is also key.
Cloud vs. on-premise advantages and draw backs.
Automation is required as well. Getting jobs to run at scheduled intervals, alert issues, built in quality assurance.
Another big topic is metadata driven applications. Re-usable frameworks.
Handling different environments: Dev; Test; Prod.
Data governance. Master Data Management. Hippa. PCI.
A resurgence of low code / no code WYSWYG web based apps built by non IT folks, doing some amazing things.
How about 3rd party vendor products, integrating into applications.
Report writers is no longer specialized enough. Business Intelligence heading that direction. More like data solutions, or data integration, or data architect solutions developer programmer lead.
You have to know your specific vendor software, plus the competitor's solutions, compare and contract benefits and downsides, plus the open source versions.
And all of that will change within three months or so.
And you have to know project life cycles and agile methodology sprints stand-up.
And version control software web based and client based.
And you have to know the coding web sites to troubleshoot issues.
And keep abreast of changes across the spectrum.
Including IDEs and development tools, and plug-ins and components, and web version cross platform.
And statistics software like R or Metlab or SSPS.
And deep learning, neural networks, speech recognition, vision, translate languages, predictors, anomaly detection, unsupervised learning.
And coding languages like python which is hot. Or Java, or c#, or Julia.
And Hive, Pig, Scoop, Flume, Oozie, Ranger, Knox.
And transfer of data rates across the network.
And ticketing software to track bugs and work.
And releasing software into production using change management frameworks.
And read blogs, magazines, journals, white papers, books, articles, documentation.
And study for certifications.
And proper email etiquette.
And interviewing skills, both as candidate and how to interview prospects.
And writing resume's.
And presentations skills. Power Points.
And documentation skills. Drawing diagrams.
And learning domain knowledge across spectrum of industries.
And estimating projects.
And chat software.
And time tracking software.
And doing webinars.
So. If you are tempted to enter the ever growing world of data, this list should give a brief indication of things to consider. It's a long journey. It never ends. You really have to enjoy what you're doing. And if you produce quality over time, there's a good change your career will flourish, and bloom.
Thanks for reading~!