100 Data Engineering Jargon That You Must Know

Data engineering is at the heart of how businesses collect, process, and use data to make informed decisions. As the field continues to grow, so does the need to understand the key terms and concepts that drive it. Whether you’re just starting in data engineering or looking to deepen your knowledge, getting familiar with the essential jargon is a must.

This list of 100 data engineering terms is designed to give you a solid foundation in the language of the field. From basic principles to more advanced techniques, these terms will help you confidently navigate the world of data engineering.

So, if you’re ready to enhance your understanding and keep up with the latest trends, read on and discover the key jargon every data engineer should know!

1. Immutable Data

Immutable data refers to data that, once written, cannot be changed or deleted. This concept ensures data consistency and integrity, often in systems requiring a reliable audit trail or in distributed computing environments where data replication and versioning are crucial.

2. Data Pipeline

A data pipeline is a series of data processing steps that automate the movement and transformation of data from one system to another. These pipelines can be designed to operate in real-time (stream processing) or in scheduled intervals (batch processing). The goal of a data pipeline is to manage the flow of data from its source to its final destination, ensuring it is ready for analysis or further processing.

3. Data Lake

A data lake is a centralized repository that stores vast amounts of raw data in its native format. It is designed to accommodate structured, semi-structured, and unstructured data, making it highly scalable. Data lakes provide a flexible storage solution, allowing organizations to store large quantities of data that can be used for various processing tasks, including advanced analytics and machine learning.

4. Batch Processing

Batch processing refers to the processing of data in large groups, or batches, at scheduled intervals. This method is ideal for handling high volumes of data that do not require immediate processing, allowing systems to process data efficiently. However, because data is processed at set times, batch processing typically introduces a delay between data generation and its availability for analysis.

5. Stream Processing

Stream processing is a method of processing data in real time as it flows into the system. This approach allows for immediate analysis and decision-making, making it suitable for applications where low latency is critical. Stream processing is continuous and often requires sophisticated infrastructure to handle the constant flow of data without interruptions.

6. Data Warehouse

A data warehouse is a large, centralized storage system that integrates data from multiple sources into a unified repository. It is specifically optimized for complex queries and reporting, enabling users to conduct in-depth analysis. Data warehouses often store historical data, allowing organizations to perform trend analysis over extended periods.

7. Data Governance

Data governance involves the management of data availability, usability, integrity, and security within an organization. It establishes a policy framework that ensures data quality and compliance with regulatory requirements. Effective data governance requires collaboration across various departments, including IT, legal, and business units, to ensure that data is properly managed and protected.

8. Data Lineage

Data lineage refers to the tracking of data as it moves through various processes and transformations within a system. It provides a historical record of where data originated, how it was transformed, and where it ultimately resides. Data lineage is crucial for auditing purposes and helps ensure transparency and trust in data-driven processes.

9. Data Transformation

Data transformation is the process of converting data from one format or structure into another, often to make it suitable for analysis. This process involves data manipulation, cleansing to remove inconsistencies, and enrichment by integrating additional information or deriving new attributes. The transformed data is then ready for further analysis or integration into other systems.

10. Data Modeling

Data modeling is the process of creating a visual representation of a data system’s structure, defining entities, relationships, and constraints. This process provides a blueprint for designing databases, ensuring that data is organized and stored in a logical, efficient manner. Effective data modeling is essential for building scalable, maintainable, and performant databases.

11. Data Mart

A data mart is a focused subset of a data warehouse that is tailored to the needs of a specific department or business unit. It is optimized for performance, providing quicker access to data and often using a simplified data model. Data marts are domain-specific, concentrating on particular subject areas such as sales, finance, or marketing, to meet the targeted analytical needs of the business.

12. Data Ingestion

Data ingestion is the process of importing, transferring, loading, and processing data from various sources into a storage system. This process can handle multiple data formats, including structured, semi-structured, and unstructured data. Data ingestion is designed to be scalable, capable of managing varying volumes of data, from real-time streams to large batch uploads, ensuring that data is ready for further processing or analysis.

13. Schema

A schema is the structural blueprint of a database, defining how data is organized, stored, and related within the system. It includes the specification of tables, fields, relationships, and constraints that ensure the integrity and consistency of the data. As databases evolve, schema management becomes crucial to maintaining the integrity of the data, requiring careful handling of schema changes over time.

14. Data Cleansing

Data cleansing is the process of identifying and correcting inaccuracies, inconsistencies, or errors in a dataset. This process ensures that the data is accurate, reliable, and ready for analysis. By removing duplicates, correcting errors, and standardizing formats, data cleansing enhances the overall quality of the data, making it more useful for decision-making and analysis.

15. Data Replication

Data replication involves the process of copying data from one database to another, ensuring that the data is available in multiple locations. This approach enhances the reliability of the data by providing backup copies that can be used in case of a failure or disaster. Data replication can be done synchronously, in real-time, or asynchronously, depending on the requirements of the system and the level of data consistency needed.

16. Sharding

Sharding is a database architecture pattern that involves splitting a large database into smaller, more manageable pieces called shards. This approach improves the performance and scalability of the database by distributing the load across multiple servers. Each shard contains a subset of the data, allowing for parallel processing of queries and transactions, which reduces the strain on individual servers and increases the overall efficiency of the system.

17. Partitioning

Partitioning is the process of dividing a large dataset into smaller, more manageable segments based on specific criteria, such as date, geographic region, or another logical attribute. This technique optimizes query performance by allowing the system to scan only the relevant partitions, rather than the entire dataset. Partitioning can be physical, involving separate storage locations, or logical, affecting only how data is accessed and organized within the system.

18. Data Integration

Data integration is the process of combining data from multiple sources into a single, unified view for analysis. This approach enables organizations to gain a comprehensive understanding of their data, allowing for more informed decision-making. Data integration often involves standardizing data formats, resolving inconsistencies, and ensuring that the integrated data is accurate and up-to-date.

19. Data Anonymization

Data anonymization is the process of removing or obscuring personally identifiable information from datasets to protect individual privacy. This technique is commonly used to comply with data protection regulations, such as GDPR, while still allowing organizations to analyze data for insights. Data anonymization strikes a balance between maintaining the usability of the data for analysis and safeguarding sensitive information from unauthorized access or misuse.

20. Data Lakehouse

A data lakehouse is a modern data architecture that combines elements of data lakes and data warehouses, allowing both structured and unstructured data to coexist in a single storage system. It offers the scalability and flexibility of a data lake while providing the data management and performance features typically associated with a data warehouse. This architecture enables organizations to run various types of analytics, including business intelligence, machine learning, and real-time processing, on a unified platform.

21. Data Virtualization

Data virtualization is the creation of a virtual layer that integrates data from multiple sources into a single, unified view without physically moving the data. This approach allows users to access and analyze data in real-time without the need for traditional data integration methods, such as ETL. Data virtualization simplifies data access, reduces storage costs, and accelerates the time-to-insight by enabling users to interact with data from diverse sources as if it were stored in a single location.

22. OLAP (Online Analytical Processing)

OLAP is a category of software tools that provides multidimensional analysis of data stored in a database, enabling users to perform complex queries and generate reports. It is typically used in data warehousing environments to support decision-making processes, allowing users to drill down into data, slice and dice it, and view it from different perspectives. OLAP systems are optimized for read-heavy operations and are designed to deliver quick responses to analytical queries, even on large datasets.

23. OLTP (Online Transaction Processing)

OLTP systems manage transaction-oriented applications, typically involving large numbers of short online transactions such as insert, update, and delete operations. These systems are optimized for fast query processing and maintaining data integrity in environments where multiple users perform transactions simultaneously. OLTP is commonly used in applications like banking, order processing, and retail, where real-time data access and reliability are critical.

24. Data Federation

Data federation is a method of integrating data from disparate sources into a virtual database, allowing users to query and analyze data without moving it from its original location. This approach provides a unified view of data across different systems, enabling seamless access to information without the complexity and cost of data consolidation. Data federation is particularly useful in environments with multiple data silos, allowing organizations to leverage existing investments while still gaining comprehensive insights.

25. Data Orchestration

Data orchestration is the automated management of data processes, including movement, transformation, and loading, across different systems and environments. It involves coordinating and optimizing data workflows to ensure that data is available at the right time, in the right format, and in the right place for analysis and decision-making. Data orchestration helps organizations streamline their data pipelines, reduce manual intervention, and improve the efficiency and reliability of their data operations.

26. ELT (Extract, Load, Transform)

ELT is a variation of the traditional ETL process, where data is first extracted and loaded into a target system, and then transformed within that system. This approach takes advantage of the processing power of modern data warehouses or cloud-based storage solutions, allowing for more efficient data transformation and greater scalability. ELT is particularly suited for big data environments, where it is often more practical to perform transformations after the data has been ingested into the target system.

27. DataOps

DataOps is a collaborative data management practice that focuses on improving communication, integration, and automation of data flows between data managers and data consumers across an organization. It combines elements of Agile development, DevOps, and data engineering to streamline data processes, reduce errors, and accelerate the delivery of data-driven insights. DataOps aims to create a culture of continuous improvement, where data quality, reliability, and responsiveness are prioritized.

28. Data Catalog

A data catalog is a detailed inventory of an organization’s data assets, providing metadata, context, and governance information about the data. It helps users discover, understand, and trust the data by offering a searchable repository that includes data descriptions, lineage, and quality metrics. Data catalogs play a crucial role in data governance and self-service analytics, enabling users to find the right data for their needs quickly and efficiently.

29. Master Data Management (MDM)

Master Data Management is the process of ensuring the uniformity, accuracy, stewardship, and accountability of shared master data assets across an organization. MDM involves creating a single, authoritative source of truth for key business entities, such as customers, products, and locations, to eliminate data silos and inconsistencies. Effective MDM improves data quality, enhances decision-making, and ensures that all parts of an organization are working with consistent and reliable data.

30. NoSQL

NoSQL refers to a class of database management systems that do not use SQL as their primary interface and are designed to handle large-scale, distributed data stores. Unlike traditional relational databases, NoSQL databases are often schema-less, allowing for greater flexibility in data modelling and storage. NoSQL is particularly well-suited for handling unstructured and semi-structured data, making it popular in big data, real-time web applications, and IoT environments.

31. Relational Database

A relational database is a type of database that organizes data into tables, which can be linked — or related — based on data common to each. This structure allows for complex queries and transactions while maintaining data integrity and consistency. Relational databases use Structured Query Language (SQL) for data manipulation and are widely used in applications where data relationships and constraints are critical.

32. Columnar Database

A columnar database stores data by columns rather than rows, which can significantly improve query performance, especially for analytical workloads. This structure allows for faster data retrieval and more efficient storage, as only the columns relevant to a query need to be accessed. Columnar databases are often used in data warehousing and big data environments, where large-scale data analysis is required.

33. Data Silo

A data silo is a situation where data is isolated within different departments or systems, making it difficult to access, share, or integrate across an organization. This fragmentation can lead to inefficiencies, inconsistencies, and a lack of comprehensive insights, as each department or system may have its own version of the data. Breaking down data silos is crucial for enabling a unified view of data and ensuring that all parts of an organization can collaborate effectively.

34. Data Reconciliation

Data reconciliation is the process of ensuring that data between different systems or databases is consistent, accurate, and up-to-date. It involves comparing and resolving discrepancies between datasets to ensure that all data sources reflect the same information. Data reconciliation is critical in environments where data is shared or integrated across multiple systems, as it helps maintain data integrity and reliability.

35. Data Munging

Data munging, also known as data wrangling, refers to the process of cleaning, organizing, and preparing raw data for analysis. This often involves transforming data into a more usable format, dealing with missing values, correcting errors, and standardizing data types. Data munging is a critical step in the data analysis process, as it ensures that the data is of high quality and ready for further processing or analysis.

36. Data Profiling

Data profiling is the process of examining data from an existing information source and summarizing information about that data, such as patterns, anomalies, and data quality. It involves analyzing the data for completeness, uniqueness, consistency, and validity to ensure that it meets the required standards for its intended use. Data profiling helps organizations understand their data better, identify potential issues, and ensure that the data is suitable for analysis and decision-making.

37. Data Masking

Data masking is the process of obscuring specific data within a database to protect sensitive information from unauthorized access. This is typically done by replacing real data with fictitious data that maintains the same format and characteristics but cannot be traced back to the original information. Data masking is commonly used in non-production environments, such as testing and development, to safeguard privacy while still allowing realistic data to be used.

38. Data Vault

Data Vault is a data warehousing methodology and architecture designed to provide long-term historical storage of data from multiple systems. It is highly scalable and flexible, allowing for the easy integration of new data sources without requiring major changes to the existing system. The Data Vault approach separates raw data (the “hub”) from relationships (the “link”) and descriptive information (the “satellite”), making it easier to manage and adapt to changing business needs.

39. Data Mesh

Data Mesh is a decentralized data architecture that aligns data ownership with domain teams, promoting scalability and reliability in data access. Unlike traditional centralized data architectures, Data Mesh allows domain-specific teams to manage their own data as products, ensuring that data is treated as a first-class citizen within each domain. This approach enables organizations to scale their data infrastructure more effectively, reduce bottlenecks, and empower teams to innovate with their data.

40. Lambda Architecture

Lambda Architecture is a data-processing architecture designed to handle massive quantities of data by using both batch and stream-processing methods. It splits data processing into three layers: the batch layer for historical data, the speed layer for real-time data, and the serving layer, which merges the results from the two. This architecture enables both real-time and batch analytics, providing a balance between latency, throughput, and fault-tolerance.

41. Kappa Architecture

Kappa Architecture is a simplified version of the Lambda Architecture where all data processing is done in real-time, eliminating the need for a separate batch layer. It relies on stream processing to handle both real-time and historical data, which are processed in the same pipeline. This approach reduces complexity and is particularly useful when real-time data is a priority and historical data processing can be handled by reprocessing streams.

42. Data Residency

Data residency refers to the physical or geographical location where data is stored, which may be subject to specific regulations or policies depending on the region. This concept is important for compliance with data protection laws, such as GDPR, that require certain types of data to be stored within specific geographic boundaries. Organizations must ensure that their data storage practices comply with local laws to avoid legal repercussions.

43. Scalability

Scalability refers to the capability of a data system to handle increasing amounts of work, or its potential to accommodate growth, without compromising performance. It can be achieved through vertical scaling (adding more resources to a single server) or horizontal scaling (adding more servers to a system). Scalability is a critical consideration in data engineering, ensuring that systems can grow and adapt to increasing data volumes and processing demands.

44. Data Minimization

Data minimization is the principle of collecting and processing only the data that is necessary for a specific purpose, often to enhance data privacy and security. This approach helps reduce the risk of data breaches and ensures compliance with regulations that limit the collection of personal information. By minimizing data collection, organizations can also streamline their data management processes and reduce storage costs.

45. Data Aggregation

Data aggregation is the process of gathering and summarizing data from multiple sources to provide a higher-level overview of information. This technique is often used to compile data for analysis, reporting, or decision-making, by combining individual data points into a more comprehensive dataset. Aggregation can help reveal trends, patterns, and insights that might not be apparent from individual data sources.

46. Data Stitching

Data stitching refers to the process of combining data from multiple sources to create a comprehensive dataset, typically used to enhance analytics and reporting. This involves linking related data points across different systems to provide a unified view of information. Data stitching is particularly useful in environments where data is scattered across various platforms and needs to be integrated for a complete analysis.

47. Data Imputation

Data imputation is the process of replacing missing or incomplete data with substituted values to maintain the integrity of a dataset. This technique is often used in data preprocessing to ensure that analyses can be conducted without bias or distortion due to missing information. Imputation methods vary from simple techniques like mean substitution to more complex statistical models that predict missing values based on other data.

48. Data Granularity

Data granularity refers to the level of detail or precision in a dataset, often determining how much information is available for analysis. Higher granularity means more detailed data, while lower granularity refers to more summarized or aggregated data. The level of granularity needed depends on the specific requirements of the analysis, with finer granularity offering more insights but also requiring more storage and processing power.

49. Data Sovereignty

Data sovereignty is the concept that data is subject to the laws and governance structures within the nation it is collected and stored. This principle often requires organizations to ensure that data handling practices comply with local regulations, especially in terms of data privacy and protection. Data sovereignty has become increasingly important with the rise of global data flows and the need to respect national laws.

50. Change Data Capture (CDC)

Change Data Capture is a technique used to identify and capture changes made to data in a database, allowing those changes to be replicated to another system. CDC is crucial in environments where real-time data synchronization and integration are required, such as in ETL processes or data replication.

51. Distributed Database

A distributed database is a database that is spread across multiple physical locations, often connected via a network. This architecture allows for data to be accessed and managed across different servers, improving reliability, scalability, and performance. Distributed databases are essential in environments where data needs to be accessible in real-time from multiple geographic locations or where large datasets need to be managed efficiently.

52. Data Wrangling

Data wrangling, also known as data munging, is the process of transforming and mapping raw data into a format suitable for analysis. It often involves cleaning data, handling missing values, and structuring the data into a more organized and useful form. This step is critical for ensuring that the data is ready for analysis, allowing for more accurate and actionable insights.

53. Event Sourcing

Event sourcing is a design pattern in which changes to application state are stored as a sequence of events, rather than overwriting the previous state. This approach provides a complete audit trail of changes, making it easier to track the history and evolution of data. Event sourcing is particularly useful in applications where it is important to maintain a history of state changes, such as financial systems or auditing tools.

54. CQRS (Command Query Responsibility Segregation)

CQRS is a design pattern that separates the responsibility of handling commands (which modify data) from queries (which retrieve data). This separation allows for optimized data handling, where different models and infrastructures can be used for reads and writes. CQRS is often used in combination with event sourcing to manage complex applications with distinct requirements for reading and writing data.

55. Dataflow Programming

Dataflow programming is a programming paradigm where the flow of data determines the execution of operations, rather than the sequence of instructions. In this model, programs are represented as directed graphs, where nodes represent operations and edges represent data dependencies. Dataflow programming is well-suited for parallel processing and is often used in data engineering to optimize the execution of large-scale data processing tasks.

56. Big Data

Big Data refers to datasets that are so large or complex that traditional data processing methods are inadequate to handle them. These datasets often require advanced tools and techniques for storage, processing, and analysis, such as distributed computing and machine learning. Big Data is characterized by the “Three Vs”: Volume, Velocity, and Variety, which describe the scale, speed, and diversity of the data.

57. Data Shuffling

Data shuffling is the process of redistributing data across different nodes in a distributed system to balance the load and optimize performance. This technique is commonly used in distributed computing frameworks like Hadoop and Spark, where data needs to be moved between nodes to ensure efficient processing. Data shuffling can introduce overhead and affect performance, so it is important to manage it carefully to avoid bottlenecks.

58. Data Sampling

Data sampling involves selecting a subset of data from a larger dataset for analysis, often to reduce the amount of data that needs to be processed. This technique is useful when working with large datasets where processing the entire dataset would be too time-consuming or resource-intensive. Sampling can be random or systematic, and it helps to maintain the representativeness of the data while reducing processing time.

59. Data Preprocessing

Data preprocessing is the step of preparing raw data for analysis, which includes cleaning, transforming, and organizing the data. This process is crucial for ensuring that the data is in a suitable format for analysis and that any issues, such as missing values or inconsistencies, are addressed. Preprocessing is often the most time-consuming part of the data analysis pipeline, but it is essential for producing accurate and reliable results.

60. Schema Evolution

Schema evolution refers to the process of managing changes to a database schema over time without disrupting existing applications. This involves adding, modifying, or removing tables, columns, and relationships while ensuring that the system continues to function correctly. Schema evolution is important in environments where data models need to adapt to changing business requirements or new data sources.

61. Eventual Consistency

Eventual consistency is a consistency model used in distributed systems where, given enough time, all replicas of the data will become consistent. This model allows for temporary inconsistencies between replicas, which can be useful in systems where availability is prioritized over immediate consistency. Eventual consistency is often used in NoSQL databases and other distributed systems where high availability is critical.

62. ACID (Atomicity, Consistency, Isolation, Durability)

ACID is a set of properties that guarantee reliable processing of database transactions. Atomicity ensures that transactions are all-or-nothing, consistency ensures that transactions take the database from one valid state to another, isolation ensures that transactions do not interfere with each other, and durability ensures that once a transaction is committed, it is permanently recorded. These properties are essential for maintaining data integrity in relational databases.

63. BASE (Basically Available, Soft state, Eventually consistent)

BASE is an alternative to the ACID properties for managing data in distributed systems, where the focus is on availability and partition tolerance rather than immediate consistency. It stands for Basically Available, Soft state, and Eventually consistent, reflecting the trade-offs made in distributed databases. BASE systems allow for more flexibility and scalability, making them suitable for large-scale applications that require high availability.

64. CAP Theorem

The CAP Theorem states that in a distributed data system, it is impossible to simultaneously provide all three of the following guarantees: Consistency, Availability, and Partition tolerance. Systems must choose two out of the three, leading to trade-offs in how data is managed. Understanding CAP Theorem is crucial for designing distributed systems that meet specific performance and reliability requirements.

65. HDFS (Hadoop Distributed File System)

HDFS is a distributed file system that provides high-throughput access to data across multiple nodes in a Hadoop cluster. It is designed to store and manage large datasets by breaking them into blocks and distributing them across the cluster, ensuring fault tolerance and scalability. HDFS is a core component of the Hadoop ecosystem and is widely used for big data storage and processing.

66. MapReduce

MapReduce is a programming model and associated implementation used to process large datasets across distributed clusters. It involves two main functions: Map, which processes input data into key-value pairs, and Reduce, which aggregates and summarizes the results. MapReduce is widely used in big data processing environments like Hadoop to perform large-scale computations efficiently.

67. Apache Spark

Apache Spark is an open-source, distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It extends the MapReduce model with additional operators and allows for in-memory processing, which can significantly speed up data processing tasks. Spark is known for its speed and ease of use, making it a popular choice for big data processing.

68. Data Sharding

Data sharding is the process of splitting a large dataset into smaller, more manageable pieces, or shards, which can be distributed across multiple servers. This approach helps to improve the performance and scalability of databases by reducing the load on any single server. Sharding is commonly used in distributed databases and applications that require high availability and quick access to large datasets.

69. Data Partitioning

Data partitioning is the technique of dividing a database into smaller, more manageable pieces called partitions, which can be distributed across different servers or storage locations. This improves performance by allowing queries to run against specific partitions rather than the entire dataset. Partitioning is often used in large-scale data environments to optimize query performance and manage data efficiently.

70. Flume

Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It is designed to handle streaming data flows, such as log files, and can be customized to support a wide range of data sources and destinations. Flume is often used in big data environments to ingest data into Hadoop or other storage systems.

71. Kafka

Apache Kafka is a distributed event streaming platform capable of handling trillions of events a day. It is used for building real-time data pipelines and streaming applications, providing a high-throughput, low-latency platform for processing and storing data. Kafka is widely used for log aggregation, stream processing, and event sourcing in modern data architectures.

72. Apache NiFi

Apache NiFi is a data integration tool that supports the automation of data flow between systems. It provides a web-based user interface for designing, monitoring, and controlling data flows, making it easy to manage the movement of data across various sources and destinations. NiFi is particularly useful for handling data in motion, with features like data provenance, security, and guaranteed delivery.

73. Data Lake Governance

Data lake governance refers to the policies, procedures, and controls used to manage and oversee the data stored in a data lake. This includes ensuring data quality, security, compliance, and proper access controls, as well as managing metadata and data lineage. Effective data lake governance is essential for maintaining the integrity and usability of the data stored in a data lake.

74. Data Provenance

Data provenance refers to the documentation of the origins, transformations, and movement of data throughout its lifecycle. It provides a historical record of where data came from, how it was processed, and where it is currently stored. Data provenance is crucial for auditing, compliance, and ensuring the trustworthiness of data in complex systems.

75. Data Stewardship

Data stewardship involves the management and oversight of an organization’s data assets to ensure data quality, privacy, and security. Data stewards are responsible for defining data policies, standards, and best practices, as well as monitoring data usage and compliance. Effective data stewardship is key to maintaining the integrity and value of data within an organization.

76. Data Swamp

A data swamp is a data lake that has become unmanageable due to a lack of proper governance, resulting in poor data quality and accessibility. Without proper organization, documentation, and metadata management, a data lake can quickly turn into a data swamp, making it difficult to find, trust, or use the data effectively. Preventing a data swamp requires strong data governance practices and regular maintenance.

77. Hyperparameter Tuning

Hyperparameter tuning is the process of optimizing the parameters of a machine learning model that are not learned from the data, such as learning rate, regularization, and the number of layers in a neural network. This process involves experimenting with different hyperparameter values to improve the model’s performance. Hyperparameter tuning is critical for achieving the best possible results from machine learning algorithms.

78. Data Serialization

Data serialization is the process of converting data into a format that can be easily stored or transmitted and then reconstructed later. Common serialization formats include JSON, XML, and binary formats like Protocol Buffers or Avro. Serialization is widely used in data engineering to transfer data between systems or store it in a compact, efficient format.

79. Data De-duplication

Data de-duplication is the process of identifying and eliminating duplicate copies of data within a dataset. This technique reduces storage requirements and improves data quality by ensuring that only unique data is retained. De-duplication is commonly used in data storage and backup systems to optimize space and improve performance.

80. Data Compression

Data compression is the process of reducing the size of a dataset by encoding it in a more efficient format. Compression can be lossless, where no data is lost, or lossy, where some data is discarded to achieve higher compression ratios. Data compression is essential for reducing storage costs and improving the performance of data transfer and processing operations.

81. Data Encryption

Data encryption is the process of encoding data to protect it from unauthorized access, ensuring that only those with the correct decryption key can read the data. Encryption is a fundamental part of data security, used to protect sensitive information both at rest (in storage) and in transit (during transmission). Strong encryption practices are essential for complying with data protection regulations and safeguarding privacy.

82. Data Tokenization

Data tokenization is the process of replacing sensitive data with non-sensitive tokens that can be used in place of the original data without exposing it to unauthorized access. This technique is often used in payment processing and other industries that handle sensitive information, allowing for secure data handling while reducing the risk of data breaches. Tokenization is an important tool for ensuring data privacy and security in modern data systems.

83. Fault Tolerance

Fault tolerance is the ability of a system to continue operating properly in the event of a failure of one or more components. This is achieved through redundancy, error detection, and error correction mechanisms that allow the system to recover from faults without significant disruption. Fault tolerance is critical in distributed systems and high-availability environments where uptime and reliability are paramount.

84. High Availability

High availability refers to the design and implementation of systems that ensure a high level of operational performance, typically characterized by minimal downtime. This is achieved through redundancy, failover mechanisms, and load balancing, allowing the system to remain functional even in the event of hardware or software failures. High availability is crucial for mission-critical applications that require continuous operation.

85. Data Latency

Data latency is the time delay between when data is generated and when it is available for processing or analysis. Low latency is often required in real-time systems where immediate data access and processing are critical, such as in financial trading or live streaming. Managing and minimizing data latency is important for ensuring timely insights and responsive applications.

86. Data Throughput

Data throughput is the amount of data that can be processed or transmitted by a system in a given amount of time. It is often measured in terms of bytes per second or messages per second and is a key performance metric for data pipelines and networks. High throughput is essential for handling large volumes of data efficiently and ensuring that systems can meet the demands of high-traffic environments.

87. Data Consistency

Data consistency refers to the accuracy and uniformity of data across a system, ensuring that all copies of the data reflect the same information. In distributed systems, maintaining consistency can be challenging, especially when dealing with network partitions or concurrent updates. Consistency models, such as strong consistency or eventual consistency, define how and when updates are propagated across the system to maintain data integrity.

88. Idempotency

Idempotency is the property of an operation that allows it to be applied multiple times without changing the result beyond the initial application. In data engineering, idempotent operations are important for ensuring that repeated actions, such as retries in a distributed system, do not introduce errors or inconsistencies. Idempotency is commonly used in APIs and data processing pipelines to provide robustness and reliability.

89. Polyglot Persistence

Polyglot persistence refers to the use of multiple types of databases or storage technologies within a single application, each chosen for its specific strengths. This approach allows developers to use the best tool for each job, such as using a NoSQL database for unstructured data and a relational database for structured data. Polyglot persistence enables more flexible and optimized data architectures, tailored to the specific needs of different parts of the application.

90. Distributed Ledger

A distributed ledger is a type of database that is shared, replicated, and synchronized across multiple nodes in a decentralized network. Unlike traditional databases, distributed ledgers do not have a central administrator and rely on consensus algorithms to ensure data integrity. Distributed ledger technology (DLT) is the foundation of blockchain systems and is used in applications that require transparency, security, and trust, such as cryptocurrency and supply chain management.

91. Graph Database

A graph database is a type of NoSQL database that uses graph structures to represent and store data, with nodes representing entities and edges representing relationships. This model is particularly well-suited for applications involving complex relationships and interconnected data, such as social networks, recommendation engines, and fraud detection. Graph databases allow for efficient querying and traversal of data, making them ideal for use cases that require exploring relationships between entities.

92. Data Caching

Data caching is the process of storing copies of frequently accessed data in a cache, or temporary storage, to reduce the time and resources needed to retrieve it. Caching improves the performance of data systems by reducing the need to access the underlying database or storage system for every request. Data caching is widely used in web applications, content delivery networks (CDNs), and distributed systems to enhance responsiveness and efficiency.

93. Schema-on-Read

Schema-on-read is a data processing approach where the schema is applied to the data at the time of reading, rather than when the data is written. This allows for greater flexibility in how data is stored, as it can be ingested in its raw form without a predefined schema. Schema-on-read is commonly used in data lakes and big data environments, where diverse data types and formats need to be accommodated.

94. Schema-on-Write

Schema-on-write is a data processing approach where the schema is defined and applied to the data at the time it is written to the storage system. This ensures that all data adheres to a predefined structure, which can simplify querying and analysis later on. Schema-on-write is typically used in relational databases and data warehouses, where data consistency and integrity are critical.

95. Data Lake Formation

Data lake formation refers to the process of creating and organizing a data lake, including the ingestion, storage, and management of raw data from various sources. This involves setting up the infrastructure, defining data governance policies, and ensuring that the data is accessible for analysis. Proper data lake formation is essential for maintaining the usability and scalability of the data lake over time.

96. Data Pipeline Orchestration

Data pipeline orchestration involves managing the execution and flow of data pipelines, ensuring that data is processed in the correct order and at the right time. Orchestration tools automate the scheduling, monitoring, and coordination of data tasks across different systems. This is crucial for managing complex workflows in data engineering, where multiple dependencies and steps need to be handled efficiently.

97. Data Observability

Data observability is the practice of monitoring and understanding the health and performance of data systems, including the quality, reliability, and flow of data. It involves tracking key metrics, such as data latency, throughput, and errors, to ensure that data systems are operating as expected. Data observability is essential for maintaining the trustworthiness of data and enabling proactive management of data infrastructure.

98. Data Skew

Data skew occurs when the distribution of data across partitions in a distributed system is uneven, leading to imbalances in processing workloads. This can result in performance bottlenecks and inefficiencies in parallel processing environments such as Hadoop or Spark.

99. Zookeeper

Apache Zookeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. It is commonly used in distributed systems to manage and coordinate the distributed processes, ensuring reliability and stability in the cluster.

100. Snowflake Schema

A snowflake schema is a type of database schema that is an extension of the star schema, where dimensional tables are normalized into multiple related tables, resembling a snowflake shape. This design reduces data redundancy but can increase the complexity of queries, making it suitable for certain types of data warehousing scenarios.

Keep this list handy as you grow in your career, and revisit it to reinforce your knowledge. With these terms under your belt, you’re well on your way to mastering the art and science of data engineering.