A considerable confusion prevails with job seekers regarding data science, big data, and data analytics for preferring a career role. Experts in data engineering, data science, data analytics, data mining, and many related fields in data science often work side by side with respective individual functions, but mistakenly many people interchange functional roles in these fields.
Let us look at the Data Science roles of Big Data Developer, and Big Data Analyst to figure out who they are and what they are used for, and the skills you need to expertise in either of these data fields.
Data Science deals with both unstructured and structured data, and everything needed for the arrangement, analysis, and cleansing of the data. Statistics, mathematics, data capturing, programming, data cleaning, problem-solving, collecting and altering data, and investigating phenomena from diverse perspectives are all part of this field. In plain terms, this field is a combination of many techniques applied in picking up data information and its insights.
Gartner defines Big Data as “High-volume, and high-velocity and/ or high variety information assets that need forms of information processing that are cost-effective and innovative and can allow enhanced decision making, insight and process automation.” This massive data volumes become difficult for processing by the conventional techniques. Non-aggregated data provides the beginning point for Big Data processing, which is challenging to store in a single computer’s memory. Every day, Big data floods businesses with huge amounts of unstructured and structured data. Some process is needed to analyze insights from such Big Data to promote good decision-making to make business fruitfully move forward as strategically planned.
Data Analytics deals with mechanical processes or algorithmic applications in developing insights from Big data. Almost all businesses use Data Analytics for their better decision-making and verify or disprove prevailing models and approaches. Data Analytics centers on inference and concentrates principally on the interpretation that mainly depends on the analyst’s knowledge.
Sources of Big Data
Big data, the huge data sets generated on the internet are gathered commercially or through free services. This data can be from any source such as a post on the sites of social networking, sensors, online videos, digital images, online purchase transaction records, mobile phones, traffic on different websites, email messages, etc. The essential characteristic of such massive data is that once the data added remains over the cloud forever and remains unremovable. And thus big data can include electronically stored information of online and offline data and information from personal devices, and even information considered removed.
Another significant contributor of big data is the evolution of sensory systems online, such as some dedicated apparatus and some others like tablet computers and smartphones. And then various connected devices to the Internet of Things that yield enormous business and personal applications. These applications can be home automation sensors connected to the internet, health monitoring, driving or driver assistance, child-care and seniors-care, and so on.
The big data evolution into recognizable information enabled the making of open-access systems for forecast services development. Analysis of the huge information available on the web as big data makes risk assessment possible to improve competitiveness. This leads to demand-driven or application-driven forecasting, a field that receives many benefits from massive data analytics giving the best insights to good decisions.
Data mining and utilizing big data
Data mining processes inter-data relations by analyzing connected dots between them to find concealed values and relations that can give new insights. This process makes available much-needed information to any business to explore ways to decrease their costs and increase revenue. This becomes a game-changer with big data cloud repository turning into an actionable technology for those who can harness it to their advantage. Data mining analyzes the relationship between inter-data with the connected dots and finds meanings that can provide new insights.
What is your role as a Big Data Developer?
Your assets as a Big Data Developer enables companies in delivering high-quality IT Service Management practices. As a Big Data Developer, your goal is to figure out how Big Data can be used as a better alternative to traditional methods in the market. This follows the core factors of Big Data, awareness of various distributed computing program models, and their management. You also have to figure out how the NoSQL database works exceptionally and effectively when correlated with alternatives. You have to possess insight on growing, debugging, optimizing, and setting up the different Big Data programs like Pig, Spark, Map Reduce, and Hive, and interpreting the theories referred to the Big Data Administration. Big Data Developer can also be known as IT Engineers, Data Warehouse Developers, Software Developers, and build their career in Big Data and Hadoop, and more.
A big data developer knows the technologies such as Apache Spark or Hadoop and knows how to process parallel data. From the perspective of programming, the focus is on Scala, Python, and Java. He knows functional programming applications. He possesses a deep knowledge of the big data platforms ecosystem and the tools that can transfer data to a big data platform or address stream processing.
A Big Data Developer closely functions with a big data systems engineer that knows Hadoop and other similar technologies, hardware requirements, and resource management. He also works with Big Data Analysts that have a good understanding of statistics and mathematics and machine learning algorithms for applying to the data provided by a big data developer.
What are the requisites for a Big Data Developer?
As a Big Data Developer, you need to possess a proven comprehension of big data applications. Good knowledge of SQL, Node.js, core Java, JS, OOAD, and other similar scripting languages. A familiarity tools that are used in big data development such as R and Python. A database foundation structures, principles, theories, and analytical, problem-solving, MapReduce and code writing skills.
The main skill you need to have is to know how the machine is performing the tasks given to it and its implications. The other key skills for developing big data systems include the knowledge of the transaction size, the paths to follow from the data or a command, the time to travel the bandwidth, latency, and data size. This must be linked to seeing how different languages or systems work at lower stages and the lines that various algorithms require taking. It all includes language/library/system implementation skills, I/O for storage, and networking at every level.
What is your role as a Big Data Analyst?
Big Data, a collection of a huge amount of data, needs appropriate database management systems for its analysis to derive useful insights. Analysis and insights from this data are considered Big Data Analytics. This aims to solve queries and become a business-friendly tool for decisions. Also, the usage of queries and various processes related to Data Aggregation is a part of Data Analytics.
Businesses use big data to better understand and target their respective customers. Using big data enables a retailer to anticipate what products sell, a telecomcarrier to predict when a customer might switch carriers, and the likes. There are many fields where big data analytics help improve businesses and human lives alike.
Big data analytics enable find new cures and predict the spread of a disease, help police catch criminals and even predict criminal activity, enable credit card companies to detect fraudulent transactions. A number of smart cities use big data analytics where a bus knows to wait for a delayed train and where traffic signals predict traffic volumes and operate to minimize jams. Big data is important to everyone due to its application in almost every field, and affecting everyone’s life in one way or the other.
Can a Big Data Analyst become a Big Data Developer?
It is not difficult for a Big data Analyst to become a Big data Developer. Analytics deals with maths and statistics. Development deals with the software skills along with the maths. Since you as an Analyst has maths skills, you need to learn to program and know the technologies used in analytics.
The difference between a Big Data Developer and a Data Analyst
Big Data Developer knew also as Data Scientist, Data Engineer, or Software Engineer generally operates under the Engineering wing. They set up the data programs that provide data to a data platform. They run programs like Hadoop, Spark, Custom Code, ETL tools, etc. to develop data pipelines for building and managing the data program. And to succeed in this task you need to be a strong technical chopper.
Data Analyst generally operates under any business stream, such as operations, strategy, product, growth, sales, marketing, etc. Data Analyst links the data platform and the business stream. They use the shared data platform and help solve business issues managing data from the data platform. They need to manage a great harmony between business and technical skills to be successful in this role.
The Data Analyst role is not an easy task
Data Analysts also take a significant part in Data Science. They play a range of tasks associated with setting up data and collecting statistical reports out of them. They further display the data in the design of tables, graphs, and charts, and apply the same to make related databases for businesses. A Data Analyst can be known as a Data Architect, a Database Administrator, an Analytics Engineer.
What are the requisites for a Big Data Analyst?
A Data Analyst conducts full life cycle activities that include requirements analysis and design, develops business intelligence and reporting capabilities, and continuously monitors performance and quality control plans to identify improvements. As a Big Data Analyst, you need to be familiar with data warehousing and business intelligence concepts, besides in-depth knowledge of SQL and analytics. You also need to have a strong understanding of Hadoop-based analytics like HBase, Hive, Mapreduce, Cascading, etc. Besides, familiarity with different ETL tools for transforming different sources of data into analytics data stores; you also require data storing and retrieving skills and tools. You have to be able to make some critical business features in real-time and decision making.
Why Big Data Analytics?
Big Data Analytics helps many businesses to improve their performance. As we all know big data consists of both structured and unstructured data from various online and offline sources, devices, sensors, instruments, etc. A Data Developer with his programming tools can gather such data from various sources, but analytics is a must for processing the data and derive some insights from such data to know what they meant and how better the information inferred can be used for business development. According to Indeed.com, the average annual salary of a Data Scientist in the U.S. is $117,000.