Hi 👋 I'm Paul Kamau
I'm a multi-cloud certified architect currently working as a Technical Account Manager for Google Cloud with resposibilities focused on Cloud Strategy and Consultancy, implementation management, advocacy and thought leadership.
Data Engineering enables data-driven decision making by collecting, transforming, and publishing data models.
A data engineer is able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability.
Data engineering techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.
Cloud Storage, Pub/Sub, Dataprep, Dataflow, DataProc, BigQuery, BQ BI Engine, Looker, Data Studio, AI Platform Notebooks
If your business doesn’t rely on data to solve problems, build revenue or track capital, then you will lose a lot
Close to 82% of businesses rely on data visualization graphics to portray business related metrics.
If data scientists are life blood of today’s data driven enterprise then data engineers are the veins carrying clean blood for machine learning algorithms to be useful.
There’s more data than ever before, and data is growing faster than ever before. 90% of the data that exists today has been created in the last two years.
Data is more valuable to companies, and across more business functions—sales, marketing, finance and others areas of the business are using data to be more innovative and more effective.
The technologies used for data are more complex. Most companies today create data in many systems and use a range of different technologies for their data, including relational databases, Hadoop and NoSQL.
Companies are finding more ways to benefit from data. They use data to understand the current state of the business, predict the future, model their customers, prevent threats and create new kinds of products. Data engineering is the linchpin in all these activities.
As data becomes more complex, this role will continue to grow in importance. And as the demands for data increase, data engineering will become even more critical.
Data Engineering solutions can unlock previously untapped solutions for your business or ideas. Therefore, a knowledgable engineer is able to:
Data analytics enables making conclusions while analyzing data from informational resources. Data analytics uses strategies via technological processes and algorithms that manipulate data for human utilization and understanding. Data analytics helps businesses optimize their capacities.
Businesses are using analytics to make more informed decisions and to plan ahead. It helps businesses to uncover opportunities which are visible only through an analytical lens. Analytics helps companies to decipher trends, patterns and relationships within data to explain, predict and react to a market phenomenon.
Collecting large amounts of data about multiple business functions from internal and external sources is simple and easy using today’s advanced technologies.
The real challenge begins, when companies struggle to infer useful insights from this data to plan for the future. Using analytics businesses can improve their processes, increase profitability, reduce operating expenses and sustain the competitive edge for the longer run.
The resources and time required for a data analytics project is dependent on a number of factors. The key factors being the scope and scale of the project, readiness and availability of required data, understanding of the analysis tools, skills and knowledge of the analytical team and most importantly, acceptance and approval from the management team to carry on the analytics project.
Pub/Sub, Dataflow, BigQuery, BQ BI Engine, Looker, Data Studio
Digital transformation has gripped the mentality of any business run today. AI services provide the output for that transformation.
Nearly 77% companies around the world have adopted AI-powered service.
If data scientists are life blood of today’s data driven enterprise then data engineers are the veins carrying clean blood for machine learning algorithms to be useful.
Here businesses can enjoy facial recognition, weapons, alcohol & drugs, search, nudity detection, celebrities, and minors turning images into insights
It allows businesses to automate communication without compromising on interactivity. Think of chatbots and smart personal assistants.
It is similar to analytic AI – it also scans huge amounts of data and searches for patterns and dependencies in it. But, instead of giving recommendations, functional AI takes actions.
These are powered with machine learning. It scans tons of data for dependencies and patterns to ultimately produce recommendations or provide a business with insights.
These are powered with machine learning. It scans tons of data for dependencies and patterns to ultimately produce recommendations or provide a business with insights.
These are powered with machine learning. It scans tons of data for dependencies and patterns to ultimately produce recommendations or provide a business with insights.
Artificial Intelligence (AI) is the concept of having machines think like humans. AI services are the performing of tasks such as reasoning, planning, learning, and understanding a particular language to scale a business further. AI services are in high demand. The brain behind artificial intelligence is a technology called Machine Learning, which is designed to make our jobs easier and more productive.
A lot of things have aligned to make this an exciting time for major advancements in AI. The processing power has improved at an amazing rate — there’s been a trillion-fold increase in performance over the last 60 years.
The cost of data processing has become more affordable. There’s more data that needs to be analysed because businesses are capturing more signals from customer interactions.
Your business can use AI services to improve consumer apps significantly — leading to further expectations in making life easier, spurring the need for greater AI technical knowhow and R&D. All this can only be achieved by hiring a robust AI service agency.
Let’s assume AI is an iceberg. What you see as a user is just the tip — but beneath the surface lurks a massive support system of our Services data scientists and engineers, along with massive amounts of data, labor-intensive extraction, data preparation, and a huge technological infrastructure.
It takes a specialized team to access the correct data, prepare the data, build the correct models, and then integrate the predictions back into an end-user Services application.
Cloud Storage, Dataprep, Dataflow, DataProc, BigQuery, AI Hub, Data Labeling, Deep Learning VM, AI Platform, TensorFlow Tool, AI Prediction Platform
The Machine Learning algorithm used by Netflix allows it to recommend personalized TV shows and movies to subscribers.
“AutoML, Google’s AI that helps the company create other AIs for new projects, learned to replicate itself in October of 2017".
You can have machine learning without sophisticated algorithms, but not without good data.
Deep learning, also known as deep neural networks, are set algorithms inspired by working principles of the human brain where it learns to identify patterns in data for decision making.
Deep learning is a subfield of representation learning, which in fact, is a subfield of machine learning.
Typically, there are 4 types of Machine Learning:
The reason for the increasing interest is due to the significant increase in data that is now available, which makes machine learning more relevant, accurate and more effective for more businesses than ever before. Machine Learning automates decisions by analysing large and diverse datasets at lightning speeds, predicting what would lead to a positive outcome and making or taking the recommended action.
Machine Learning automates decisions in a continuous improvement cycle. And machines automate actions. The combination of both greatly reduces the need for human intervention for a process to be completed. When we remove the reliance on human intervention we tend to achieve faster response times, a reduction in cost and a reduction in human error or bias for any process – from the granting of a loan to the landing of a plane.
Pub/Sub, Dataflow, BigQuery, BQ BI Engine, Looker, Data Studio
With this much data available to analyse, access and implement, there is a science needed to mould them into reports or strategies.
Nearly 80% of data available over the internet is unstructured.
“7x – the number of times your business can scale faster.”.
Data Science has transformed itself to become a more dynamic field. Data Science has rendered Business Intelligence to incorporate a wide range of business operations.
With the massive increase in the volume of data, businesses need data scientists to analyze and derive meaningful insights from the data.
The meaningful insights of Data Science will help companies to analyze information at a large scale and gain necessary decision-making strategies.
The process of decision making involves the evaluation and assessment of various factors involved in it. Factors such as, understanding the context and nature of the problem that we are required to solve, exploring and quantifying the quality of the data, implementation of the right algorithm and tools for finding a solution to the problems, can make Data Science work for you.
BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer questions like quarterly revenue analysis or business problems.
Pub/Sub, Dataflow, BigQuery, BQ BI Engine, Looker, Data Studio
If your business doesn’t rely on data to solve problems, build revenue or track capital, then you will lose a lot
Close to 82% of businesses rely on data visualization graphics to portray business related metrics.
7x – the number of times your business can scale faster.
Yes we do! We're dedicated to helping you focus on your product brand, growth and customers while we do all the heavy lifting building. You'll never have to think about servers, databases, monitoring and other technical implementations.
Yes. We will never charge you for the baseline features that are ready and available for you.
The scope of your project and requirements will influence the timeline. Typically, we can get you an MVP in less than 7 business days. Get ready to impress your prospects.
Our Pay-as-you go model is super simple and affordable. Check out our subscription packages for MVPs, mid-level and complete Web app solutions.
That's what we love to hear! Hit us up on the contacts below and feel free to ask anything else you'd like 😊.
