@jm507pty_: JAKAJA🤭 #foryou #viral #tiktok #trend #parati #xyzbca #indirectas

JM507

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Region: PA

Friday 09 September 2022 16:29:48 GMT

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Comments

José Saavedra 🤠🔥 :

jajaja pon que diga suegra 😂😂

2022-09-09 20:48:15

24

DIEGOGO :) :

@KIARA...♡ Jjsjsjssjsjsj miraaaaaaaa

2022-11-14 09:52:03

1

😮‍💨$AD BOY😮‍💨🫡 :

as uno de suegra plis

2022-11-14 14:05:32

0

︎ :

Intentando llegar a 1000 seguidores comentando

2022-11-21 15:38:20

1

The Beast Human :

@Lisette vera xD <3 yo se cocinar

2022-11-14 01:49:52

1

Soy arez :

yo sé cocinar desde pequeño me enseñó mi ama 🥺 saludos plebes 🤟🇲🇽

2022-09-23 13:07:40

1

MUEBLERÍA DAMER :

@Antony Nf jsjsjs

2022-11-14 19:21:05

1

Aldair Martínez 🥷🏻. :

aveces los hombres gritamos así😂😂😂

2022-11-04 15:47:40

0

Samu.. :

@Mi Shel°♡ mi bebe ajajja 🤣🤣

2022-11-08 14:32:21

1

Cheko Bryz Franciscool :

eso no le pasa a cualkiera

2022-12-06 23:04:31

1

➿CASTELLÓN➰ :

ami no me quiere 💔😪

2022-09-17 01:02:03

1

Gro-ver :

nuevo yerno ? osea el antigo yerno no sabia cosinar 😂😂😂 y no sabes si sabra cosinar el siguiente yerno

2022-11-21 17:51:17

0

xante 18 :

OBVII 😏🤣❤️❤️❤️❤️

2023-02-01 03:36:20

0

erick sosa :

si soy 😂

2022-11-14 21:57:40

0

Jhoset :

ajaja no te pases 😂😂

2022-11-04 23:15:58

0

Antony Martel ure745 :

jaja yo si se ...

2022-11-21 13:25:51

0

edisoncabrera818 :

Final inesperado

2022-11-12 15:21:38

0

𝔇𝔞𝔫𝔦𝔢𝔩𝔉ℜ :

ay toa yo xd

2022-11-15 04:55:14

0

Alex :

Nunca falta el grito de guerra🤑

2022-11-22 02:10:17

0

edu :

Jajajajajajaj

2022-11-21 20:09:45

0

After Dark :

todos etiquetan :( 🥺......

2022-11-14 21:06:49

0

J_A10 :

sisoy

2022-11-14 20:16:30

0

j :

ps yo ago majia en la cosina

2022-12-07 22:17:26

0

Josué.solorzano.10 :

😂😂yo si se cocinar 😏

2022-12-06 20:08:15

0

To see more videos from user @jm507pty_, please go to the Tikwm homepage.

Other Videos

Chị em bị mất ngủ, ngủ không sâu giấc lưu ý!! #tsdsngocan #whcsktt #songkhoe247

Chị em bị mất ngủ, ngủ không sâu giấc lưu ý!! #tsdsngocan #whcsktt #songkhoe247

A Structured Approach to Big Data Management: Generic Big Data Architecture In the current data-centric environment, organizations increasingly rely on robust big data architectures to efficiently manage massive datasets. A generic big data architecture serves as a blueprint, offering a structured methodology for data ingestion, processing, and analysis at scale. This article explores the key layers, components, and data flow patterns within a typical big data architecture, encompassing both batch and real-time processing scenarios. Layered Architecture for Scalable Data Management The generic big data architecture can be conceptualized through distinct layers, each addressing a crucial aspect of data management: Data Source Layer: This layer encompasses all sources of data, including relational databases, Internet of Things (IoT) devices, social media platforms, and application logs. Ingestion Layer: Data is extracted from various sources using technologies like Apache Kafka, Apache NiFi, or AWS Kinesis. This layer ensures the smooth flow of data into the architecture. Storage Layer: Data is stored in scalable and fault-tolerant storage systems like HDFS (Hadoop Distributed File System), Amazon S3, or NoSQL databases. This layer guarantees data accessibility and integrity. Processing Layer: Data is processed using either batch processing frameworks like Apache Spark and Apache Hadoop for historical analysis or real-time processing engines like Apache Flink and Apache Storm for near-instant insights from live data streams. Analytics Layer: Processed data is analyzed using tools such as Apache Hive, TensorFlow, or machine learning frameworks to extract valuable insights and patterns. Visualization Layer: Extracted insights are presented in an easily digestible format through data visualization tools like Tableau, Power BI, or Apache Superset, facilitating informed decision-making. Components for a Robust Big Data Ecosystem Each layer within the big data architecture leverages specific technologies and tools to ensure efficient data management. Here's an overview of the key components: Data Sources: Relational databases, IoT devices, application logs, social media platforms. Ingestion: Apache Kafka, Apache NiFi, AWS Kinesis, Apache Flume. Storage: HDFS, Amazon S3, Apache Cassandra, MongoDB. Processing: Apache Spark, Apache Flink, Apache Storm, Apache Beam. Analytics: Apache Hive, TensorFlow, PyTorch, scikit-learn. Visualization: Tableau, Power BI, Apache Superset, Grafana. Data Flow Patterns: Batch vs. Real-Time Processing Data can flow through the big data architecture in two primary ways: Batch Processing Flow: Data is ingested, stored, processed in batches, analyzed, and finally visualized. This approach is ideal for historical data analysis and generating reports. Real-Time or Stream Processing Flow: Data is continuously ingested and processed in real-time, enabling near-instantaneous insights and driving automated actions based on live data streams. This approach is crucial for applications requiring immediate response to data changes. Conclusion: Unlocking the Power of Data A generic big data architecture offers a structured framework for managing and processing vast datasets effectively. Understanding the various layers, components, and data flow patterns is instrumental for organizations seeking to leverage the power of their data. By adopting the right technologies and best practices, businesses can unlock valuable insights, make data-driven decisions, and gain a competitive edge in today's data-driven landscape.

A Structured Approach to Big Data Management: Generic Big Data Architecture In the current data-centric environment, organizations increasingly rely on robust big data architectures to efficiently manage massive datasets. A generic big data architecture serves as a blueprint, offering a structured methodology for data ingestion, processing, and analysis at scale. This article explores the key layers, components, and data flow patterns within a typical big data architecture, encompassing both batch and real-time processing scenarios. Layered Architecture for Scalable Data Management The generic big data architecture can be conceptualized through distinct layers, each addressing a crucial aspect of data management: Data Source Layer: This layer encompasses all sources of data, including relational databases, Internet of Things (IoT) devices, social media platforms, and application logs. Ingestion Layer: Data is extracted from various sources using technologies like Apache Kafka, Apache NiFi, or AWS Kinesis. This layer ensures the smooth flow of data into the architecture. Storage Layer: Data is stored in scalable and fault-tolerant storage systems like HDFS (Hadoop Distributed File System), Amazon S3, or NoSQL databases. This layer guarantees data accessibility and integrity. Processing Layer: Data is processed using either batch processing frameworks like Apache Spark and Apache Hadoop for historical analysis or real-time processing engines like Apache Flink and Apache Storm for near-instant insights from live data streams. Analytics Layer: Processed data is analyzed using tools such as Apache Hive, TensorFlow, or machine learning frameworks to extract valuable insights and patterns. Visualization Layer: Extracted insights are presented in an easily digestible format through data visualization tools like Tableau, Power BI, or Apache Superset, facilitating informed decision-making. Components for a Robust Big Data Ecosystem Each layer within the big data architecture leverages specific technologies and tools to ensure efficient data management. Here's an overview of the key components: Data Sources: Relational databases, IoT devices, application logs, social media platforms. Ingestion: Apache Kafka, Apache NiFi, AWS Kinesis, Apache Flume. Storage: HDFS, Amazon S3, Apache Cassandra, MongoDB. Processing: Apache Spark, Apache Flink, Apache Storm, Apache Beam. Analytics: Apache Hive, TensorFlow, PyTorch, scikit-learn. Visualization: Tableau, Power BI, Apache Superset, Grafana. Data Flow Patterns: Batch vs. Real-Time Processing Data can flow through the big data architecture in two primary ways: Batch Processing Flow: Data is ingested, stored, processed in batches, analyzed, and finally visualized. This approach is ideal for historical data analysis and generating reports. Real-Time or Stream Processing Flow: Data is continuously ingested and processed in real-time, enabling near-instantaneous insights and driving automated actions based on live data streams. This approach is crucial for applications requiring immediate response to data changes. Conclusion: Unlocking the Power of Data A generic big data architecture offers a structured framework for managing and processing vast datasets effectively. Understanding the various layers, components, and data flow patterns is instrumental for organizations seeking to leverage the power of their data. By adopting the right technologies and best practices, businesses can unlock valuable insights, make data-driven decisions, and gain a competitive edge in today's data-driven landscape.

#messi #ميسي #football #explore #foryou #viral #foryoupage #اكسبلور #اكسبلورexplore #تصميم #كوره #كره_القدم #fypシ #🇦🇷 #m10 #barcelona #edit

#messi #ميسي #football #explore #foryou #viral #foryoupage #اكسبلور #اكسبلورexplore #تصميم #كوره #كره_القدم #fypシ #🇦🇷 #m10 #barcelona #edit

I'm going to be punished by my neighbors if I don't play anymore #game #steamgame

I'm going to be punished by my neighbors if I don't play anymore #game #steamgame

M1 chúc mọi người an lành #xuhuongtiktok #tiepthilienket #methaocuasuatit

M1 chúc mọi người an lành #xuhuongtiktok #tiepthilienket #methaocuasuatit

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