Best Database and SQL Courses to Learn Online | Simpliv
HBase – The Hadoop Database
Prerequisites: Working with HBase requires knowledge of JavaRecord and run settings a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs.
Relational Databases are so stuffy and old! Welcome to HBase – a database solution for a new age.
HBase: Do you feel like your relational database is not giving you the flexibility you need anymore? Column oriented storage, no fixed schema and low latency make HBase a great choice for the dynamically changing needs of your applications.
What’s Covered:
- 25 solved examples covering all aspects of working with data in HBase
- CRUD operations in the shell and with the Java API, Filters, Counters, MapReduce
- Implement your own notification service for a social network using HBase
- HBase and it’s role in the Hadoop ecosystem, HBase architecture and what makes HBase different from RDBMS and other Hadoop technologies like Hive
- Using discussion forums
- Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(
- We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices
- The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person.* The truth is, direct support is hugely expensive and just does not scale.
- We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose
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Hadoop, MapReduce for Big Data problems
Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.
This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel.
Let’s parse that.
Zoom-in, Zoom-Out: This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other.
Hands-on workout involving Hadoop, MapReduce : This course will get you hands-on with Hadoop very early on. You’ll learn how to set up your own cluster using both VMs and the Cloud. All the major features of MapReduce are covered – including advanced topics like Total Sort and Secondary Sort.
The art of thinking parallel: MapReduce completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to “think parallel”.
What’s Covered: Lot’s of cool stuff ..
Using MapReduce to:
Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm.
Build an Inverted Index for Search Engines: Use MapReduce to parallelize the humongous task of building an inverted index for a search engine.
Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text.
Build your Hadoop cluster:
Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes
Set up a hadoop cluster using Linux VMs.
Set up a cloud Hadoop cluster on AWS with Cloudera Manager.
Understand HDFS, MapReduce and YARN and their interaction
Customize your MapReduce Jobs:
Chain multiple MR jobs together
Write your own Customized Partitioner
Total Sort : Globally sort a large amount of data by sampling input files
Secondary sorting
Unit tests with MR Unit
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SQL And Databases – Heavy Lifting
Prerequisites: No prerequisites are needed for the SQL commands and DBMS fundamentals. Basic knowledge of programming in Python would be helpful if you want to run the source code in the course-ending project.
Taught by Stanford-educated, ex-Googlers. This team has decades of practical experience in quant trading, analytics and e-commerce.
Your bodyguard for when data gets too big, this course is strong but friendly, funny yet deep, animated yet thoughtful.
Let’s parse that.
Your bodyguard for when data gets too big: Most business folks (and quite a few engineers) use Excel as a basic tool of decision making and modeling, but when you can’t fit the data you’d like into an Excel spreadsheet that you can easily open, its time to move to a database.
The course is strong but friendly: This course will help you move to a database without being intimidated by the new environment. Don’t let anyone tell you that any dataset is too large or too complicated for you to understand (and people will try, most likely)
The course is funny yet deep: It goes really deep into the topics that folks often find hard to understand, such as joins, aggregate operators and interfacing with databases from a programming language. But it never takes itself too seriously:-)
The course is very visual : most of the techniques are explained with the help of animations to help you understand better.
This course is practical as well : Queries are explained in excruciating detail, indices are demystified, and potentially career-limiting traps (Drop, Alter) are marked with bright yellow tape markers so you can steer clear.
The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art – all shown by studies to improve cognition and recall.
What’s Covered:
- SQL In Great Depth
- Database Fundamentals and Just Enough Theory
- Practical Examples – Queries in MySQL and SQLite, and code in Python
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Complete Google Data Engineer and Cloud Architect Guide
This course is a really comprehensive guide to the Google Cloud Platform – it has 25 hours of content and 60 demos.
The Google Cloud Platform is not currently the most popular cloud offering out there – that’s AWS of course – but it is possibly the best cloud offering for high-end machine learning applications. That’s because TensorFlow, the super-popular deep learning technology is also from Google.
What’s Included:
- Compute and Storage – AppEngine, Container Enginer (aka Kubernetes) and Compute Engine
- Big Data and Managed Hadoop – Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub
- TensorFlow on the Cloud – what neural networks and deep learning really are, how neurons work and how neural networks are trained.
- DevOps stuff – StackDriver logging, monitoring, cloud deployment manager
- Security – Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
- Networking – Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
- Hadoop Foundations: A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)
- Yep! Anyone looking to use the Google Cloud Platform in their organizations
- Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP
- Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud
- Yep! Anyone looking to build TensorFlow models and deploy them on the cloud
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Date and Time Fundamentals in .NET and SQL Server
Teaching Approach
No fluff, no ranting, no beating the air. I esteem your time. The course material is succinct, yet comprehensive. All the important concepts are covered. Particularly important topics are covered in-depth. For absolute beginners, I offer my help on Skype absolutely free, if requested.
Take this course, and you will be satisfied.
Build a solid foundation of working with Date and Time with this course
Almost all the software works with date and time in one form or another. Some programs, of course, need to work with date and time more than other. However, date and time hide many pitfalls even in very simple scenarios. We all know that security and logging are regular aspects of almost any program. There is a wise phrase that sounds like: “Developers recall about security when it is too late.” The same can be said about date and time. Most developers don’t even imagine how many bugs are hiding behind the simple face of date and time. Those bugs are sitting in programs and waiting for their moment to blow up your production code causing very unfortunate repercussions. That’s why working with date and time is the full-fledged and very important aspect of software development.
Have you watched magnificent enlightening TV Shows by BBC? This course in some parts is like those BBC shows, because you’ll learn many interesting surprising facts. Besides, being familiar with peculiarities of working with date and time, you can write much more reliable and maintainable applications.
No fluff, no ranting, no beating the air. I esteem your time. The course material is succinct, yet comprehensive. All the important concepts are covered. Particularly important topics are covered in-depth. For absolute beginners, I offer my help on Skype absolutely free, if requested.
Take this course, and you will be satisfied.
Build a solid foundation of working with Date and Time with this course
Almost all the software works with date and time in one form or another. Some programs, of course, need to work with date and time more than other. However, date and time hide many pitfalls even in very simple scenarios. We all know that security and logging are regular aspects of almost any program. There is a wise phrase that sounds like: “Developers recall about security when it is too late.” The same can be said about date and time. Most developers don’t even imagine how many bugs are hiding behind the simple face of date and time. Those bugs are sitting in programs and waiting for their moment to blow up your production code causing very unfortunate repercussions. That’s why working with date and time is the full-fledged and very important aspect of software development.
Have you watched magnificent enlightening TV Shows by BBC? This course in some parts is like those BBC shows, because you’ll learn many interesting surprising facts. Besides, being familiar with peculiarities of working with date and time, you can write much more reliable and maintainable applications.
Click here continue to improve your Knowledge
Databases In The Cloud With AWS RDS
In the past decade, cloud computing has been gaining popularity at a tremendous rate. Some cloud providers are experiencing a growth rate of 50% year over year – which is just astounding. And the reason for this growth is obvious – cloud computing enables ubiquitous, convenient, on-demand access to a shared pool of configurable computing resources which can be rapidly provisioned and released with minimal effort.
Amazon Web Services is a cloud computing platform that offers a broad set of global compute, storage, database, analytics, application, and deployment services that help organizations move faster, lower IT costs, and scale applications.
Ever since its inception – the relational database management systems have gained tremendous popularity across the world. Recently NoSQL databases like MongoDB has also gained a lot of traction – but still, RDBMS remains the de-facto choice of engineers when it comes to storing structured data. According to some estimates – relational databases are used in more than 90% of the software projects out there.
With the advent of cloud computing – solution designers and architects had to deal with some unique challenges while attempting to migrate their relational databases to the cloud. You see, relational databases need high-performance hardware and disks to perform at the peak level. But IaaS cloud computing services provide us with virtual servers – which store their data on network connected disks. So to manage relational data in the cloud – we needed a specialized PaaS (platform as a service) which provided adequate hardware and redundancy to relational databases.
Amazon Relational Database Service (or Amazon RDS) is a distributed relational database service by Amazon Web Services (AWS). AWS RDS is a web service running “in the cloud” designed to simplify the setup, operation, and scaling of a relational database for use in applications. Complex administration processes like patching the database software, backing up databases and enabling point-in-time recovery are managed automatically. Scaling storage and compute resources can be performed by a single API call.
Introduction of AWS RDS along with its unique features has made it very simple for us to deploy and grow relational databases to a very large scale in the cloud. What’s more – the tight integration that exists between most of the AWS services – makes it seamless to deploy multi-tier complex and scalable applications.
This course will give you in-depth knowledge about AWS RDS and demonstrate some key features and guidelines you need to know to get started with it. We will kick things off with an overview of AWS RDS. After covering the basics of AWS RDS – we will dive deep and learn how to setup highly available, scalable and robust relational database clusters which span multiple AWS availability zones.
Do you know what it takes to design and deploy sophisticated cloud-based relational databases?
How does one go about securing the databases and scaling them for a global audience?
How to make for disaster recovery and failovers?
How to scale the infrastructure with a simple push of a button or a simple command?
These are some of the fundamental problems Cloud engineers, architects and database administrators struggle with on a daily basis.
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