Data volumes have grown tremendously over the past few years. Data has been generated more rapidly in the last few years than in all of human history up to that point. Worldwide Big data revenue has grown exponentially, and many companies are embracing Big data to gain a competitive edge and creating new avenues for innovation and disruption. This article discusses the top Big data trends that businesses should watch to strengthen and secure their disrupted business models. Top 7 Big Data Trends to Dominate 2021, DASCA
Robotic process automation promises to free business users from tedious, repetitive tasks and deliver fully automated enterprise solutions, but this promise may be shortsighted. There is a lot more that can be accomplished with RPA–especially when combined with data science. This article offers some insights on how businesses leveraging RPA can now go a step ahead and integrate data science techniques into their processes to develop highly intelligent automation processes, thereby making it easier to deploy data science models in production. When RPA meets data science, InfoWorld
Organizations are always searching for tools and platforms that can help their analysts be more efficient in their jobs. As data science tools and platform development matures, an effective data science tool in the hands of capable data scientists; it can make the difference between success and failure. Finding the proper data science tools is paramount if you want your team to uncover business insights. This article discusses some of the things that businesses need to look for when they search for their next data science platform. 5 Things to Look for in Your Next Data Science Platform, TDWI
In the rapidly evolving world of the internet, mobile devices, and artificial intelligence, we are witnessing some impressive leaps forward in technology. Having access to the enormous amount of data, it is imperative for businesses to utilize data science tools in order to discover insights from the data. This blog sheds some light on the benefits of using data for research & development. The Future Is Now: Why Data Is Key to Tech Research & Development, insideBIGDATA
In today’s ever-changing business landscape, it’s evident that leveraging data to derive meaningful insights is no longer a competitive advantage but a necessity for making timely decisions and staying relevant. This article discusses how embracing the cloud will improve data engineer productivity and reap the benefits of real-time, data-driven insights. To Unleash Data Potential, Enterprises Need to Fully Embrace the Cloud, TDWI
Generally, business executives, analysts, and product managers monitor metrics over time. In the data science world, time series analysis is a critical component of work. Monitoring metrics across thousands of these dimension values and their combinations manually is basically impossible. This is where timeseries analysis comes in handy. This article discusses timeseries analysis, particularly timeseries anomaly detection on multi-dimensional business data. Also, it explains how CueObserve, an open-source metrics monitoring system, is solving anomaly detection at scale. Running Timeseries Anomaly Detection at Scale on SQL Data, Towards Data Science
With the rise of cloud computing technology, Database-as-a-service (DBaaS) service enables users to access and use a cloud database system without having to install or maintain any database software. This article discusses DBaaS and its core benefits in the context of a distributed cloud architecture. Database-as-a-Service: A Key Technology for Agile Growth, THE NEW STACK
Information retrieval is a critical function of data engineering, where search engines play a crucial role. This article sheds some light on some of the key milestones, important characteristics, and challenges in the evolution of search engine architecture. Evolution Of Search Engines Architecture – Algolia New Search Architecture Part 1, High Scalability
In the pursuit of the data quality initiative, Airbnb added data quality checks (i.e., data quality, accuracy, completeness, and anomaly checks) as part of the Airflow DAG. This blog post sheds some light on the challenges Airbnb faced while adding an enormous number of data checks to prevent data bugs and how that motivated Airbnb to build a new framework to easily add data checks at scale. How Airbnb Built “Wall” to prevent data bugs, Airbnb
Perhaps one of the best illustrations of the real-world complexity of data engineering m can be found in the blog. This blog outlines the various stages of a data team as an organization grows and serves as a catalyst for bringing a data-driven culture to an organization. Building a data team at a mid-stage startup – a short story, Erik Bernhardsson.
Hope you enjoy reading our Sparity News roundup — August 2021.
We’ll see you next month with more.