About a decade ago, when the data science jobs started going mainstream, there was a flood of opportunities in the tech world. However, most companies didn’t understand what to make of it. At one of my earlier stints, I used to hear phrases repeatedly, _we’re doing big data _and _we’re doing data science. _Because it was advertised that data scientists get big paychecks, data analysts, database administrators, data engineers — all of the wanted to be data scientists; without an understanding of what it requires to be one.
This is not the age of specialization. One needs to be a generalist who specialises in something. Just like life. One can be a neurosurgeon and still drive a car. It’s not odd to find a data engineer and a data scientist both in the same person, but it’s highly unlikely to see it in practice because it’s too broad an area of responsibility. Similarly, it’s highly unlikely to find a neurosurgeon at night who drives an Uber during the day.
Specialization is for insects — Robert A. Heinlein
Being a data engineer and a data scientist, both in one, also comes with a challenge of diving into the vast ocean of knowledge in both these fields related to data. A data engineer should be able to do basic data sciency stuff and a data scientist should be able to do basic data engineering. The same can be said about other fields of software. As in, the data engineer should be able to do basic frontend work and so on.
Having said that, it’s not so much that the skill is the distinguisher between all these fields, rather it is the thought process.
It doesn’t matter so much what you think, but how you think it — Christopher Hitchens
One of my managers used to make an interesting analogy of data engineering with plumbing. Data engineers move data from one place to another. Just like a cooking gas or drinking water need a pipeline to move from the plant to your house, the data needs a pipeline to move from one system to another. At the risk of sounding rude and engineer-splaining, I don’t want to carry forward with this analogy but it is rather true if you think about it.
Data engineers are the plumbers building a data pipeline, while data scientists are the painters and storytellers, giving meaning to an otherwise static entity — Dave Bianco
Data engineers are plumbers. But they are also more than that. In addition to making sure that data is transported from one place to another, data engineers make sure that the quality of data is good for use.
They also gauge how the data is going to be used and based on that they make decisions on how to store it, how best to retrieve it, to process it and so on. Some examples are choosing between traditional relational databases, data warehouses and NoSQL data stores or choosing between columnar and row-oriented data stores, choosing task schedulers, choosing data processing infrastructure.
While a data engineer might be a plumber, a data scientist is the one who accesses the water through the plumbed pipes and makes lemonade.
Read Robert Chang’s three piece introduction to data engineering.
Let’s come to the main point of difference between a data engineer and a data scientist. Obviously, the job titles are different, the KRAs are different but they can surely overlap. The main quality that distinguishes these two creatures is how they think.
A data engineer thinks in terms of movement, strictness, predictability, cleanliness and resilience — of the data and, of the systems carrying the data.
There’s a striking difference between how these two approach handling data — movement of data, for example, should have the quality of being deterministic. If some data is supposed to arrive from one location to another, it should. If a transformation was to be applied to a dataset for cleaning or modification, it should happen. Data engineering, in that sense, should be predictable, dependable, resilient — Deterministic.
#software-development #thinking #data-science #data-engineering #machine-learning #data analysis
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt
CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data
Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:
Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.
#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data