ProjectPro

JUNE 6, 2025

With the global data volume projected to surge from 120 zettabytes in 2023 to 181 zettabytes by 2025, PySpark's popularity is soaring as it is an essential tool for efficient large scale data processing and analyzing vast datasets. They are distributed across the cluster, enabling efficient data processing at scale.

Hadoop 68

Hadoop Metadata Java Datasets 68

Confluent

JUNE 13, 2019

The data processing pipeline characterizes these objects, deriving key parameters such as brightness, color, ellipticity, and coordinate location, and broadcasts this information in alert packets. The data from these detections are then serialized into Avro binary format.

Kafka 102

Kafka Bytes Data Pipeline Transportation 102

ProjectPro

JUNE 6, 2025

If input data violates the validation rules, Pydantic raises an error. Source: LinkedIn Pydantic AI vs Crew AI Pydantic AI focuses on robust data validation and parsing for Python applications. Built on Pydantic, it simplifies handling complex data schemas with automatic type validation and error handling.

Building 40

Building Pipeline-centric Database-centric Data Validation 40

ProjectPro

JUNE 6, 2025

Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. HBase storage is ideal for random read/write operations, whereas HDFS is designed for sequential processes. Data Processing: This is the final step in deploying a big data model. How to avoid the same.

Big Data 40

Big Data Hadoop Relational Database AWS 40

Confluent

APRIL 30, 2019

This problem is not new in data processing. Although the Kafka Streams library is “data schema agnostic” today and therefore cannot leverage many standard techniques from the query processing literature, such as predicate pushdown, there is still a large optimization room on structural topology formation for it to explore.

Kafka 91

Kafka Coding Bytes Software Engineering 91

Ascend.io

MAY 24, 2023

To mitigate this, in Python v2, we replaced the intermediate processing batches with Parquet storage and loaded the table once into the database, rather than after each batch. This strategy dramatically reduced processing time and network costs. Our answer to this challenge lay in big data processing.

Healthcare 52

Healthcare Data Pipeline Hospitality MySQL 52

ProjectPro

NOVEMBER 22, 2021

show(truncate=False) #Drop duplicates on selected columns dropDisDF = df.dropDuplicates(["department","salary"]) print("Distinct count of department salary : "+str(dropDisDF.count())) dropDisDF.show(truncate=False) } Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Q6.

Hadoop 52

Hadoop Metadata Java SQL 52