Module 1: Data fundamentals you need to know
Roadmap to become a Data Engineer
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Hey troubleshooters! Welcome to the new series where we gonna explore the basics of data terminologies which would ultimately make the trajectory of stepping into the data world bit easier. In order to get a better understanding you can check out the previous blog I'm linking the blog down here:
So let's get started with some basic terminologies one by one:
DATA ARCHITECTURE
Data architecture is the framework for managing and organizing an organization's data.
It involves designing databases and data storage systems, defining data standards and protocols, and ensuring data security.
Data architects work with stakeholders to understand their data needs and develop solutions that provide accurate and timely information.
Data architecture is important for decision-making and compliance with regulations.
It is an ongoing process that requires continuous monitoring and updating.
DATA MODELLING
Data modeling is the process of creating a conceptual, logical, and physical representation of data.
The goal of data modeling is to organize and structure data in a way that is useful for analysis, decision-making, and business operations.
The process involves identifying entities, attributes, relationships, and constraints that define the data and its structure.
There are different types of data models, including conceptual, logical, and physical models.
Conceptual models provide a high-level view of the data and its relationships, while logical models provide more detail on the attributes and relationships.
Physical models define how the data is stored in a database, including tables, columns, and relationships.
Data modeling is an iterative process that requires ongoing refinement and validation as new data is collected and requirements change.
Effective data modeling can help organizations make better decisions, improve data quality, and streamline business processes.
DATA PREPROCESSING
Data preprocessing is the process of cleaning, transforming, and preparing raw data before analysis.
The goal of data preprocessing is to improve data quality, reduce noise and bias, and prepare the data for analysis.
Common techniques include data cleaning, normalization, feature scaling, dimensionality reduction, and handling missing values.
Data preprocessing can be time-consuming and requires careful consideration of the specific dataset and analysis goals.
The process involves identifying and handling missing data, dealing with outliers, and addressing inconsistencies in the data.
Feature selection and scaling can also be performed as part of data preprocessing.
DATA EXTRACTION
Data extraction is the process of retrieving data from various sources, such as databases, files, and websites.
The goal of data extraction is to obtain the necessary data for analysis and decision-making.
Data extraction can involve structured or unstructured data and may require data integration to combine data from multiple sources.
Techniques for data extraction include using application programming interfaces (APIs), web scraping, and database queries.
Data extraction can be automated using tools and software that streamline the process and reduce manual effort.
The quality of the extracted data can impact the accuracy and effectiveness of data analysis and decision-making.
Data extraction is an important step in the data management and analysis process and requires careful consideration of the sources and types of data involved.
DATA TRANSFORMATION
Data transformation converts raw data into a suitable format for analysis.
Techniques include data cleaning, normalization, aggregation, and feature engineering.
Data cleaning identifies and corrects errors, inconsistencies, and missing values.
Normalization scales data to a common range.
Aggregation combines data from multiple sources or reduces granularity.
Feature engineering creates new features or transforms existing ones.
Data transformation is time-consuming and requires careful consideration of the dataset and analysis goals.
Quality data transformation improves data analysis and decision-making.
DATA LOADING
Data loading is the process of inserting data into a target system for analysis and decision-making.
Techniques for data loading include batch processing, real-time processing, and incremental loading.
Data mapping, validation, and integrity are important considerations for successful data loading.
Automation tools can streamline the data-loading process and reduce manual effort.
The quality of the loaded data impacts the accuracy and effectiveness of analysis and decision-making.
Careful consideration of the target system and data source is required for successful data loading
DATA VISUALISATION
Data visualization presents data and information graphically for easy understanding.
Techniques include charts, graphs, maps, and infographics.
Effective visualization considers audience, message, and data type.
Visualization can reveal insights into patterns, trends, and relationships.
Tools include software and programming languages like Tableau, Power BI, and Python.
Interactive visualization facilitates real-time data exploration and analysis.
Quality visualization impacts the accuracy and effectiveness of analysis and decision-making.
Visualization is an important step in the data management and analysis process, helping organizations make better decisions and improve operations.
DATA LAKE
A repository for raw and unstructured data from various sources.
Can store data without pre-defined schema or transformations.
Stores structured, semi-structured, and unstructured data from IoT, social media, logs, etc.
Provides a flexible and scalable approach to data storage and analysis.
Can be implemented on-premise, in the cloud, or in a hybrid model.
Proper data governance and management are crucial.
Tools and technologies include Hadoop, Spark, Amazon S3, and Azure Data Lake Storage.
Helps organizations improve decision-making and productivity through faster data access and advanced analytics.
Success depends on factors such as data architecture, management, governance, and user adoption.
DATA WAREHOUSING
Collects, stores, and manages data from multiple sources for analytical reporting and decision-making.
Data is transformed, integrated, and structured in a central location optimized for query and analysis performance.
Typically stores historical data to provide a long-term perspective on business operations.
Uses ETL processes to move data from source systems to the data warehouse.
Data is organized into dimensional models such as star and snowflake schemas to support analysis.
Enables organizations to gain insights, identify trends, and make informed decisions.
Requires proper planning, design, implementation, and maintenance.
DATA MINING
Discovers hidden patterns and insights in large datasets using statistical and machine learning algorithms.
Can be supervised or unsupervised, and applied to various types of data including text, images, and videos.
Techniques include classification, clustering, association rule mining, and anomaly detection.
Results can improve business processes, support decision-making, and make predictions.
Requires expertise in statistical analysis and machine learning algorithms.
Privacy and ethical concerns should be taken into account.
Tools include R, Python, SAS, and IBM SPSS.
DATA SUMMARISATION
Data orchestration automates data flow between systems, applications, and technologies.
It includes data integration, pipeline management, and workflow automation.
Used for data analytics, machine learning, and artificial intelligence applications.
Tools include Apache Airflow, Apache NiFi, and Kubernetes.
Requires planning, design, and monitoring.
Data governance and security considerations are important.
Ensures data privacy and compliance with regulations.
DATA STORAGE
Data storage is the process of storing and managing data in a structured manner.
It involves selecting appropriate storage technologies and implementing storage architectures to meet data access and retrieval requirements.
Common types of data storage include databases, file systems, and cloud storage services.
Data storage solutions must consider factors such as scalability, security, and cost-effectiveness.
Proper data storage is essential to support data-driven decision-making and ensure data availability and integrity.
DATA COMPUTATION
Data computation refers to the process of performing calculations and analyses on data to extract meaningful insights.
It involves using computational tools and techniques such as statistical analysis, machine learning, and artificial intelligence algorithms to process and analyze large volumes of data.
Data computation can be performed using various programming languages and frameworks, including Python, R, and Spark.
The results of data computation can be used to support decision-making, improve business processes, and make predictions.
Proper data governance and security measures must be taken into account to ensure data privacy and compliance with regulations.
DATA MANAGEMENT
Data management refers to the process of collecting, storing, organizing, maintaining, and utilizing data effectively.
It involves implementing policies, procedures, and technologies to manage the lifecycle of data and ensure its quality, accuracy, and security.
Data management includes various processes such as data governance, data quality management, data security, and data privacy.
Proper data management is essential to support data-driven decision-making, improve business processes, and comply with regulations.
Data management solutions can include a combination of software tools, hardware, and personnel to manage data effectively.
DATA PROCESSING
Data processing transforms raw data into useful information.
It involves stages such as data collection, cleaning, integration, aggregation, and analysis.
Tools and techniques used for data processing include SQL, Excel, Python, and R.
Results can be used to support decision-making and improve business processes.
Data governance and security measures are essential for ensuring data privacy and compliance with regulations.
Big data processing technologies like Hadoop, Spark, and MapReduce can handle large volumes of data efficiently.
DATA PIPELINE
A data pipeline moves data from the source to target systems for analysis or storage.
It includes data ingestion, processing, storage, and delivery stages.
Tools and technologies used for data pipeline design include Apache Kafka, Airflow, and AWS Glue.
Data pipelines improve data quality, reduce time to insights, and increase productivity.
They require skilled professionals to set up and maintain and must adhere to data governance and security measures.
DATA DEPLOYMENT
Data deployment is the process of taking processed data and putting it into use in a production environment.
This process includes testing, validation, and documentation to ensure accuracy and quality.
Continuous integration and continuous deployment (CI/CD) can be used to automate the deployment process.
Security measures must be taken into account to protect sensitive data and comply with regulations.
Ongoing monitoring and maintenance are essential to ensure the accuracy and quality of the deployed data.
Successful data deployment requires collaboration between data engineers, data scientists, and end-users.
DATA FILE FORMATS
Data file formats are standardized ways of organizing and storing data in a file.
Common data file formats include CSV, Excel, JSON, XML, Parquet, and Avro.
CSV is a plain text file format that uses commas to separate values in tabular data.
Excel is a proprietary binary or CSV-like file format used by the Excel spreadsheet application.
JSON is a lightweight data interchange format that uses a text format to represent data structures.
XML is a markup language that uses tags to define elements and attributes to define properties.
Parquet is a columnar storage file format optimized for reading large, complex datasets.
Avro is a binary serialization system that supports schema evolution and efficient data compression.
DATA SOURCE
A data source is the origin or location of data.
It can be physical or digital and can include databases, files, APIs, sensors, or human input.
Data sources can be internal or external to an organization.
They can be structured or unstructured.
Effective management of data sources is important for ensuring the quality, security, and reliability of data used in decision-making and analysis.
DATA EXPLORATION LIBRARIES
Pandas: a Python library for data manipulation and analysis, providing data structures for efficiently storing and manipulating data, as well as functions for data cleaning, filtering, and grouping.
NumPy: a Python library for numerical computing that provides support for multi-dimensional arrays and matrices, along with a wide range of mathematical functions and linear algebra operations.
Matplotlib: a Python library for creating static, animated, and interactive visualizations, including line charts, scatter plots, bar charts, histograms, and more.
Seaborn: a Python library for data visualization that is built on top of Matplotlib, providing a higher-level interface for creating more complex and aesthetically pleasing visualizations.
DATA FRAMEWORK
Apache Hadoop: a distributed data processing framework for large-scale datasets.
Apache Spark: a fast and general-purpose cluster computing system for programming entire clusters with implicit data parallelism.
Apache Flink: a distributed data processing framework for streaming and batch processing of large datasets with low-latency and high-throughput data processing.
Apache Storm: a distributed real-time computation system for processing streaming data with fault tolerance, reliability, and scalability.
Apache Kafka: a distributed streaming platform for real-time data pipelines and streaming applications with reliable, scalable, and high-throughput messaging and storage of data.
METADATA
Metadata refers to data that describes other data, and it plays a crucial role in data management and governance.
Metadata provides information about data types, formats, sources, lineage, ownership, and usage, among other things.
Metadata can be structured or unstructured, and it can be stored in metadata repositories or data catalogs.
Metadata management involves capturing, storing, and maintaining metadata to ensure its accuracy, completeness, and relevance.
Metadata can be used to support a range of data engineering activities, such as data integration, data quality, data lineage, and data discovery
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