The Work Data Engineers Do
This article has been written not only for those in the Data Analytics field but also for those who want to know more about Data Engineering without the benefit of prior knowledge of the fundamental concepts of Data Science. The interest in Data Science is growing astronomically; this is a humble attempt to help bridge the existent knowledge gap.
Garbage In = Garbage Out
Data Science and Analytics has become one of the ‘smart’ jobs for new age people – everyone and his dog (so it seems) want to get into Data Analytics. There are several career choices within the field of Data Science, I have been working as a Data Engineer with Aptus Data Labs for __ years, based at their Hyderabad office. In response to the many questions I have received on what Data Engineers do, I thought it prudent to publish this blog that sets out the range of work of a Data Engineer; it’s easier to coherently explain this work in the form of a ‘blog’ so that I don’t have to stammer through a speech (lol). As you’ve no doubt guessed, Data Engineering is all about collecting, organizing and securely warehousing DATA so that it is available in a useful form for the Analysts, Data Scientists, Machine Learning Engineers and other Data specialists to use in order to produce predictions, to prescribe the ways that an organisation can achieve growth targets and to produce insightful information upon which important business decisions can be made. The work does not only involve the creation of a good Data Warehouse (DWH), ongoing maintenance of the DWH is equally important (remember that old adage: “garbage in = garbage out”); maintaining the Data Warehouse is an ongoing challenge.
Glamour in this job ?
For glamour, one has to become a famous cricketer or a Bollywood actor, but it is my experience that a Data Analytics professional is increasingly seen as an important person in society
The top companies provide an envious workplace to IT employees – the working conditions and perks are some of the best for new age people in comparison to most other entry-level jobs. The question that nags me, however, is – why are so many Software Developers switching to Data Analytics? After all, there are new protocols to learn, quite often new programming languages to be learnt, new tools to be mastered, and yet many are making the switch. Although I don’t have a simple answer to this, perhaps the lack of qualified Data Analytics people may be the reason that software developers are getting into this niche field as they have a head start with their experience in coding, some level of data knowledge, the use of algorithms, and somewhere at the back of their minds the knowledge of mathematical and quantitative techniques learnt at university.
Data or Datum?
Data is the fuel that powers Data Analytics, Machine Learning, and several of the evolving technologies such as Artificial Intelligence (AI). Speaking of AI, it is exciting to note that Data Engineers make a stellar contribution in environments where AI is the core function. Thinking of driverless cars, aren’t you? Imagine working as a Data Engineer in such cutting-edge companies like Tesla and Google!
Other areas of Data Engineering effectiveness are in Natural Language Processing (NLP) platforms such as Alexa & Google Assistant, Cortical Learning and Deep Learning platforms like Google Brain. Of course, advanced robotics cannot proceed without the work of good Data Engineers – exciting indeed is the range of ‘new age’ applications that cannot go an inch forward without the back-office work of Data Engineers. The advent of data starts from the tips of our fingers (or retina of the eyes or even our face through facial recognition applications) when we write, read, select something on our devices, fill in a form, visit places of interest or simply take a picture. The data can travel any distance, thousands of miles even, through switches and routers, undersea cables and large data centres, and lodge into the on-site server (or cloud platform) of the entity that has prompted the collection of this data.
Organizations use Data Ingestion and streaming tools for achieving a suitable framework that can collect, import, load, transfer, integrate, and process data from a range of data sources. Data Engineers are responsible for configuring these ingestion workflows with the Data Warehouse for storage of voluminous amounts of data with either distributed batch processing, or real-time processing and for analytical computation platforms. Data Engineers have the added responsibility of ensuring that the streaming pipelines are fault-tolerant. Fault tolerance refers to the design and architecture of the streaming pipeline that anticipates and mitigates problems so that the said problems are not encountered, upstream or downstream, which would negatively impact the data service being provided. The design must support exactly-once processing semantics to guarantee that each record will be processed once (repeat processing may lead to chaos) even in the event of failures at stream client or broker end, while ensuring milli-second processing latency. Perhaps one way to understand this is the tension one experiences when one uses one’s credit card for online payment; interference at that moment could possibly mean that your account has been debited but the seller’s account not credited – and you have to make another payment – double payment; the payment portal has to be designed with robust fault tolerance so that the payment is processed once, and only once! Data Engineers maintain a high performance distributed computing environment; tools such as Hadoop, Vertica Analytical platform, Cassandra, MongoDB, RapidMiner Server, PowerBI, R-Studio server, and some others, are commonly used to organise and ingest data. These tools possess the ability to transform data into meaningful insights that can help businesses make data-driven decisions.
An important aspect of the design and maintenance of the Data Warehouse is that of data security. Imagine that your personal information stored by one of the online retailers is being breached, or mixed with someone else, or even stolen – it would put you in a vulnerable situation that could lead to huge troubles for you. Data security is non-negotiable, and an
important part of the Data Engineer’s competency. Proper Data Governance is formulated and maintained so that SLA’s are met, the architecture is scalable, and disaster management is ensured.
The Art of Juggling Unstructured Data
Data Scientists are usually quite innovative in ‘playing’ data for new insights; at its peak, insights, and decisions are produced in microseconds. This super quick response time is entirely due to the optimized design and integration of Big Data Systems set up and maintained by Data Engineers. Note, however, that not all data are in a structured form – there are voluminous amounts of data out there that is unstructured. Let’s agree that almost every organisation, small to large, has a presence in social media such as Facebook, Instagram, LinkedIn, Twitter, etc. Every tweet or Instagram picture can attract excessive responses from followers – and this can go ‘viral’ quickly. Social media junkies abound out there – they like to respond to everything put out in social media. Guess what, these responses in text, text speak, emojis, pictures, soundbites, etc, are vital feedback data to the company; it informs the company of the feelings of ordinary people towards the company, a product, or its conduct. This is the kind of vital insight that is sorely needed by the company in order to proceed in the right way to achieve success; however, the response data on social media can be quite unstructured – especially emojis, pictures, textspeak and soundbites. This is where Custom Data Ingestion comes to the fore. Data Engineers would typically use NoSQL transformation pipelines to ingest, manage, and optimize such data so that data technology operates smoothly. Emails is also another source of unstructured content – PDF documents, pictures, soundbites, text speak, etc, may be found in emails and such data is also a wealth of information that is worth mining. Integration of Social Media and email APIs with in-memory accelerated Data Science computing frameworks like Kafka, Spark, and Apache Ignite help achieve better 360-degree user support with quicker and smarter automated responses. The use of NoSQL distributed computing database clusters helps organizations process and analyse call-centre audio and video recordings with Pattern Recognition and Natural Language Processing models to achieve quality audits and understand customer needs.
Data Management in Cloud Platforms
With the help of Big Data architecture, Data Engineers maintain and integrate the large collections of Big Data processing tools and platforms. It is a challenge, indeed, to ensure that proper and efficient multi-platform workflows are run efficiently with low latency. Data Engineers are also trained in cloud platforms integration and cloud migrations to facilitate the deployment of projects on cloud platforms with ease. A good Data Engineer is always ready to accept new user challenges and handle the computational requirements for each project. With new business imperatives, Data Engineers are now equipped with the knowledge of advanced technologies such as Hybrid Multi-Cloud. Handling Elastic Compute Cloud, choosing the appropriate AWS storage option, analyses of cloud economics, ensuring proper cloud security, managing data warehousing using Amazon Redshift, maintaining cheap data archival,
integrating AWS Deep Learning AMIs – we Data Engineers do it all. Finally, we regulate (as if waving it ‘goodbye’) processed data as it departs for the customer-based application in a data mart, ensuring data integrity and in-wire encryption.
Key Data Engineering Competencies
Data Engineering is one of the keys to successful and innovative Data Science applications. When the foundation of a building is sound, the building is able to boast its magnificence even in the most severe weather conditions; the converse could be quite devastating. Data Engineering is similar – it is the bedrock for using data smartly to grow an organisation, to improve the efficiency and effectiveness of the organisation and for exceeding goals and objectives of the organisation. Up to 60% of the work in a Big Data Analytics environment is that of Data Engineering. In the era of constant new technologies hitting us from all sides, it is the dour application, methodical approach, and sheer focus of Data Engineers that ensures that Data Scientists and Analysts of their ilk are able to successfully tame data. Considering a career as a Data Engineer like myself? Get a good quality qualification to rise above the rest. I do recommend AptusLearn as we have top class professionals to guide you on the practical side of the lessons