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What are the essential components of a big data storage and processing architecture?

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Big data is a term that refers to the massive volume, variety, and velocity of data that is generated from various sources and needs to be stored, processed, and analyzed efficiently. Big data storage and processing architecture is the design and implementation of the systems and components that enable big data solutions. In this article, you will learn about the essential components of a big data storage and processing architecture and how they work together to deliver value.

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1 Data sources

Data sources are the origins of the data that is collected and ingested into the big data storage and processing architecture. Data sources can be structured, semi-structured, or unstructured, and can come from various domains such as web, social media, sensors, transactions, logs, etc. Data sources can also have different formats, such as text, images, audio, video, etc. Data sources can be internal or external to the organization, and can have different levels of quality, reliability, and security.

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2 Data ingestion

Data ingestion is the process of acquiring, transforming, and loading the data from the data sources into the big data storage and processing architecture. Data ingestion can be batch or stream, depending on the frequency and latency of the data. Batch ingestion involves moving large volumes of data at regular intervals, such as daily or weekly. Stream ingestion involves moving continuous flows of data in real time or near real time. Data ingestion can also involve various techniques such as filtering, cleansing, validating, enriching, and encrypting the data.

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3 Data storage

Data storage is the component that provides the physical or logical space to store the data that is ingested into the big data storage and processing architecture. Data storage can be on-premise or cloud-based, and can have different characteristics such as capacity, scalability, availability, durability, and cost. Data storage can also have different types, such as relational databases, NoSQL databases, data warehouses, data lakes, file systems, object stores, etc. Data storage can also support different access methods, such as SQL, APIs, or Hadoop.

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4 Data processing

Data processing is the component that provides the computational power and logic to manipulate, analyze, and extract value from the data that is stored in the big data storage and processing architecture. Data processing can be batch or stream, depending on the use case and the requirements. Batch processing involves running complex and intensive operations on large volumes of data at scheduled times, such as ETL, data mining, machine learning, etc. Stream processing involves running fast and lightweight operations on continuous flows of data in real time or near real time, such as event processing, anomaly detection, sentiment analysis, etc.

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Saad Abdul Rauf

AWS 解决方案架构师 |云工程师|数据架构师、工程师、分析师和教练 |帮助组织建立繁荣所需的数据驱动文化。
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Data processing is indeed crucial, but let's also highlight the importance of data preprocessing. Before diving into complex operations, data must be prepared. Cleaning noisy data, handling missing values, and scaling features are essential. Think of it as cleaning and peeling vegetables before cooking; it ensures a smoother and tastier meal. Data preprocessing sets the foundation for accurate and meaningful results in your big data journey.

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5 Data analytics

Data analytics is the component that provides the tools and techniques to explore, visualize, and communicate the insights and outcomes from the data that is processed in the big data storage and processing architecture. Data analytics can be descriptive, diagnostic, predictive, or prescriptive, depending on the level of complexity and value. Data analytics can also involve various methods, such as statistics, data mining, machine learning, artificial intelligence, etc. Data analytics can also use different formats, such as reports, dashboards, charts, graphs, etc.

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Saad Abdul Rauf

AWS 解决方案架构师 |云工程师|数据架构师、工程师、分析师和教练 |帮助组织建立繁荣所需的数据驱动文化。
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Yes, all that you have said is correct, In addition to data analytics, user-friendly data visualization is crucial. It simplifies complex data into charts and graphs, aiding easy understanding. Visualizing data helps identify trends and make informed decisions, making it an integral part of any big data system

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6 Data governance

Data governance is the component that provides the policies, standards, and best practices to ensure the quality, security, privacy, and compliance of the data that is stored and processed in the big data storage and processing architecture. Data governance can also involve various roles and responsibilities, such as data owners, data stewards, data engineers, data analysts, data scientists, etc. Data governance can also use various tools and techniques, such as data catalog, data lineage, data quality, data security, data privacy, data audit, etc.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Saad Abdul Rauf

AWS 解决方案架构师 |云工程师|数据架构师、工程师、分析师和教练 |帮助组织建立繁荣所需的数据驱动文化。
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In addition, it's crucial to address data ethics and compliance. Big data often involves sensitive information, so ensure that your architecture includes measures for anonymization, consent management, and adherence to data protection regulations like GDPR. Prioritize responsible data handling to build trust with users and avoid legal issues.

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What are the essential components of a big data storage and processing architecture?

1

2

3

4

5

6

7

1 Data sources

2 Data ingestion

3 Data storage

4 Data processing

5 Data analytics

6 Data governance

7 Here’s what else to consider

Data Warehousing

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What are the essential components of a big data storage and processing architecture?

1

2

3

4

5

6

7

1 Data sources

2 Data ingestion

3 Data storage

4 Data processing

5 Data analytics

6 Data governance

7 Here’s what else to consider

Data Warehousing

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