Crafting a narrative with data is a journey that typically doesn't initiate with oneself. The data's origin is crucial, and the effort to make it usable often extends beyond individual capabilities, especially within an enterprise context.
Contemporary applications and projects are often extensive and complex, requiring a collaborative effort from individuals with diverse skills and knowledge. Each person contributes unique talents, collectively working to coordinate tasks and responsibilities, guiding a project from conception to fruition.
In the recent past, roles like business analysts and business intelligence developers were standard for processing and comprehending data. However, the proliferation of data in terms of volume and types has led to the evolution of these roles into specialized skill sets, enhancing and streamlining the processes of data engineering and analysis.
Business Analyst
Although data analysts and business analysts share some similarities, the crucial distinction lies in how they utilize data. A business analyst has a closer alignment with the business and specializes in interpreting data derived from visualization. It's common for an individual to take on both the roles of a data analyst and a business analyst.
Data Analyst
Data analysts play a crucial role in helping businesses extract maximum value from their data assets using tools like Microsoft Power BI. Their duties involve profiling, cleansing, and transforming data, as well as constructing scalable semantic models. Data analysts implement advanced analytics features into reports, collaborating with stakeholders to define data and reporting needs, and then translating raw data into meaningful insights.
The management of Power BI assets, encompassing reports, dashboards, workspaces, and underlying semantic models, falls under the data analyst's purview. They are responsible for implementing security measures, aligning with stakeholder requirements, to safeguard Power BI assets and associated data.
Collaboration with data engineers is vital for data analysts to identify suitable data sources that meet stakeholder criteria. Additionally, they work alongside data engineers and database administrators to secure proper access to required data sources. Data analysts also contribute to identifying new processes or enhancing existing ones for efficient data collection and analysis.
Data Engineer
Data engineers provision and set up data platform technologies that are on-premises and in the cloud. They manage and secure the flow of structured and unstructured data from multiple sources. The data platforms that they use can include relational databases, nonrelational databases, data streams, and file stores. Data engineers also ensure that data services securely and seamlessly integrate across data platforms. Primary responsibilities of data engineers include the use of on-premises and cloud data services and tools to ingest, egress, and transform data from multiple sources. Data engineers collaborate with business stakeholders to identify and meet data requirements. They design and implement solutions. While some alignment might exist in the tasks and responsibilities of a data engineer and a database administrator, a data engineer's scope of work goes well beyond looking after a database and the server where it's hosted and likely doesn't include the overall operational data management.
A data engineer significantly enhances the effectiveness of business intelligence and data science initiatives. Through the process often referred to as data wrangling, where the engineer consolidates diverse data sets, projects gain momentum, allowing data scientists to concentrate on their specialized tasks.
Collaboration between data engineers and data analysts is crucial. As a data analyst, close coordination with a data engineer ensures seamless access to a variety of structured and unstructured data sources. The data engineer's role extends to supporting analysts in optimizing semantic models, typically hosted in modern data warehouses or data lakes.
The transition to a data engineer role is feasible for individuals with backgrounds as database administrators or business intelligence professionals. However, successful transition requires acquiring proficiency in the tools and technologies essential for handling substantial data volumes.
Data Scientist
Data scientists engage in sophisticated analytical processes to derive insights from data, covering a spectrum from descriptive analytics, conducted through exploratory data analysis (EDA), to predictive analytics utilized in machine learning. Descriptive analytics involves evaluating data to reveal patterns.
The predictive analytics component in machine learning employs modeling techniques for anomaly detection and pattern recognition, contributing significantly to forecast models. However, these aspects represent only a portion of a data scientist's responsibilities.
Beyond descriptive and predictive analytics, data scientists may delve into the intricacies of deep learning. In this realm, they conduct iterative experiments, employing custom algorithms to address intricate data challenges. This multifaceted approach showcases the diverse and dynamic nature of a data scientist's role.
Empirical observations indicate that a significant portion of tasks within a data science project involves activities like data wrangling and feature engineering. The efficiency of data scientists in conducting experiments is notably enhanced when data engineers proficiently handle the intricacies of data wrangling.
While superficially, the roles of a data scientist and a data analyst may appear distinct, this perception is inaccurate. A data scientist engages with data to identify pertinent questions, form hypotheses, and design experiments. Subsequently, the data scientist collaborates with a data analyst to leverage their expertise in data visualization and reporting, underscoring the interconnected nature of their work.
Database administrator
A database administrator is tasked with implementing and overseeing the operational aspects of cloud-native and hybrid data platform solutions, specifically those utilizing Microsoft Azure data services and Microsoft SQL Server. Their responsibilities encompass ensuring the continuous availability, consistent performance, and optimization of these database solutions. Collaborating with stakeholders, they identify and enforce policies, tools, and procedures for data backup and recovery.
Contrasting the role of a database administrator with that of a data engineer, the former focuses on monitoring and managing the overall health of a database and its underlying hardware. In contrast, a data engineer engages in data wrangling, encompassing activities such as ingesting, transforming, validating, and cleaning data to align with business needs.
Moreover, the database administrator holds the additional responsibility of safeguarding the data's overall security. This includes overseeing user access, as well as granting and restricting privileges based on business requirements.
SWETA SARANGI
16.02.2024