# Difference Between Big Data VS Data Science

Big data vs data science – understand the key differences between these two fields. By comparing these two fields you can reveal how big data’s size and speed contrast with data science’s modeling and algorithms.

This guide provides an in-depth comparison of big data and data science – their relationship, implications, overlap of methods and technologies, and areas of diversion. It also covers how they complement each other in driving data-centric innovation.

## What is Big Data?

Big data involves huge volumes of highly variable, complex data that is generated and processed very rapidly from a diverse range of sources. Processing such massive, heterogeneous datasets requires new technologies and methods to manage and analyze the information.

Some key characteristics of big data are:

- Extremely large data volumes requiring massively parallel software and hardware for storage and processing.
- High velocity – streaming data that needs to be analyzed and acted on in real-time.
- Wide variety in data formats – text, images, video, audio, sensor data, clicked, spatial, logs etc.
- Complexity in linking, matching, cleansing data across systems and sources.
- Growing need for new technologies and analytical methods to handle data at scale.

Big data requires rethinking data management, infrastructure, and analysis to handle the data deluge and extract timely insights.

## What is Data Science?

Data science revolves around extracting useful information from raw data by using statistics, scientific methods, algorithms, and processes.

Characteristics of data science are:

- Application of statistics, predictive modeling, machine learning techniques to tackle real-world problems using data.
- Analytics life cycle processes like CRISP-DM to frame problems, collect, prepare, analyze, visualize and deploy data.
- Focus on advanced analytical capabilities like classification, prediction, clustering, anomaly detection, sentiment analysis etc.
- Skills in programming languages like Python, R, Scala to work with data.
- Collaborative discipline bridging business requirements, analytical expertise, software engineering and subject matter experience.
- Communication of data insights visually and as data products to drive business value.

Data science provides the comprehensive framework to make use of data for strategic, tactical and operational decision making.

## Key Differences Between Big Data and Data Science

Big data focuses on data engineering at scale, whereas data science performs advanced analytics to create business value. Here is a head-to-head comparison of big data vs data science.

Parameter | Big Data | Data Science |
---|---|---|

Focus | Handling large data volumes and varieties | Advanced analytical modeling and predictions |

Emphasis | Data engineering – storage, movement, processing | Data analysis – statistics, machine learning |

Key Challenge | Scalability and performance | Relevance of analysis to solving business problems |

Infrastructure | Distributed systems – Hadoop, Spark etc. | Cloud platforms – AWS, GCP, Azure |

Methods | Databases, data warehouses, data lakes | Statistical models, algorithms like SVM, neural nets, random forests |

Data Scope | Batch and real-time data | Retrospective and current data |

Data Orientation | Schema-less | Feature engineering |

Key Roles | Data engineer, architect | Data scientist, machine learning engineer |

Key Outputs | Data storage, processing pipelines | Analytical models, visualizations and insights |

Data Quality Sensitivity | Fault tolerant of poor data | Requires curated, high quality data |

## Areas of Convergence

Despite the fundamental differences, big data and data science converge in some technology domains:

**Distributed Systems**

Apache Hadoop, Spark, Kafka provide scalable data storage and streaming for both big data and data science workloads.

**Cloud Infrastructure**

Platforms like AWS, GCP and Azure offer on-demand big data and data science capabilities.

**Data Lakes**

Central repositories build from disparate sources provide the foundation for managing large volumes of structured and unstructured data.

**Data Pipelines**

Automated ETL processes acquire, transform and move data between systems to enable analysis.

**Data Visualization**

Platforms like Tableau, PowerBI help explore, understand and present both big data sources and analytical outputs.

**Statistical Methods**

Foundational statistical thinking in terms of distributions, hypothesis testing, confidence intervals etc. inform both domains.

**Streaming Analytics**

Emerging capabilities to analyze and act on real-time data streams at scale supports both big data and predictive models.

## Comparing Evolution

Both big data and data science have rapidly gained prominence driven by the confluence of key factors:

**Big Data Drivers**

- Exponential growth in data volumes and sources
- Maturing open source distributed systems like Hadoop and Spark
- Emergence of cloud computing with virtually unlimited storage and processing
- Internet of Things and growth in sensors and connected devices
- Enhancing networks enabling real-time data transfer and analysis

**Data Science Drivers**

- Evolution of advanced machine learning algorithms and libraries like PyTorch, TensorFlow etc.
- Vastly increased statistical, mathematical and programming capabilities
- Growth of packed analytical tools like Python, R, Jupyter notebooks
- General purpose cloud platforms providing easy access to programming environments
- Increasing business orientation – focus on operationalizing analytical models

## Comparing Methodologies

The workflows for typical big data and data science projects also highlight their distinct approaches:

**Big Data Methodology**

- Identify sources and estimate volume, variety and velocity of incoming data
- Architect and design scalable distributed data storage and processing infrastructure
- Extract data from sources and load into distributed file systems or data lakes
- Perform data cleaning, transformations and aggregation using MapReduce or Spark
- Build data cubes, indexes and metadata to structure and summarize data
- Develop capabilities to analyze batch and real-time data at scale
- Present data visualization dashboards and reports summarizing key metrics

**Data Science Methodology**

- Frame the business problem to be solved or key insights required
- Identify, acquire and explore relevant structured and unstructured data
- Clean, prepare and preprocess data for analysis by handling outliers, missing values etc.
- Perform statistical analysis like regression analysis, sentiment analysis to understand relationships
- Engineer features from raw data that can inform predictive modeling
- Train machine learning models on sample data using algorithms like SVM, random forest etc.
- Rigorously evaluate models for accuracy, errors and overfitting
- Interpret outputs and extract key business insights
- Operationalize models by integrating them into business applications and processes
- Monitor and retrain models continuously with new streaming data

## Career Transitions

The convergence of technologies enables some movement across the domains:

**Big Data to Data Science**

Here, picking up statistical modeling, ML techniques and business acumen are key focus areas.

**Data Science to Big Data**

For this transition, gaining data engineering skills around distributed systems, pipeline development, stream processing is crucial.

**Emerging Crossover Roles**

New roles like Machine Learning Engineer, Data Ops Engineer, Data Platform Architect capabilities from both areas.

## Future Outlook

As data volume and complexity increases exponentially, integration between big data and data science capabilities will grow across these dimensions:

- Unified cloud-based platforms providing end-to-end capabilities from data processing to advanced analytics. AWS, Databricks and Snowflake are early examples.
- Automation, low code tools will enable non-experts to use data science for rapid application development.
- With real-time data streaming, big data capabilities will need to be tightly coupled with analytical models and event triggers.
- Rise of new disciplines like MLOps and DataOps focused on increased production of analytical models.
- Organizational integration as dedicated data teams merge into enterprise data competency centers.
- Enhanced governance as data quality, lineage, privacy and ethics become critical with increasing business reliance on analytics.

## Key Takeaways

- Although related, big data and data science are fundamentally distinct fields.
- Big data enables storing, processing, and organizing large volumes of data leveraging distributed systems.
- Data science applies advanced analytical modeling using statistical and machine learning techniques to extract value from data.
- They share some common technologies around data pipelines, platforms, storage, and visualization. But the core focus differs.
- Big data provides the data foundation whereas data science offers analytical capabilities to transform data into value.
- As organizations become data-driven, integration between the two areas will continue to grow across infrastructure, platforms, roles and processes.

## Conclusion

Big data and data science represent two distinct areas of study that enable organizations to make use of the power of data and analytics at scale. They originated differently, exponential data growth has driven convergence across tools, platforms and responsibilities.

Big data allows storing and processing large, diverse data sets across distributed systems. Data science performs advanced analysis using statistical and machine learning algorithms to extract strategic value from data.

They diverge fundamentally in focus, intent, tools and methods. However, for an integrated analytics ecosystem, they complement each other’s capabilities spanning data volume, variety, velocity and analytical sophistication.

As data becomes central to decision making and value creation, capabilities from both domains will increasingly coalesce. However, they will continue to maintain distinct identities and centers of gravity.

**More to read**

- Introduction to Data Science
- Brief History of Data Science
- Components of Data Science
- Data Science Lifecycle
- Data Science Techniques
- 24 Skills for Data Scientist
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- Statistics for Data Science
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- Data Science Interview Questions and Answers
- Data Science Vs. Artificial Intelligence
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- Best Books to learn Python for Data Science
- Best Books on Statistics for Data Science