article thumbnail

Cutting Big Data Costs: Effective Data Processing With Apache Spark

DZone

In today's data-driven world, efficient data processing plays a pivotal role in the success of any project. Apache Spark , a robust open-source data processing framework, has emerged as a game-changer in this domain.

Big Data 279
article thumbnail

Batch Processing for Data Integration

DZone

In the labyrinth of data-driven architectures, the challenge of data integration—fusing data from disparate sources into a coherent, usable form — stands as one of the cornerstones. As businesses amass data at an unprecedented pace, the question of how to integrate this data effectively comes to the fore.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Data Mesh?—?A Data Movement and Processing Platform @ Netflix

The Netflix TechBlog

Data Mesh?—?A A Data Movement and Processing Platform @ Netflix By Bo Lei , Guilherme Pires , James Shao , Kasturi Chatterjee , Sujay Jain , Vlad Sydorenko Background Realtime processing technologies (A.K.A Last year we wrote a blog post about how Data Mesh helped our Studio team enable data movement use cases.

article thumbnail

2. Diving Deeper into Psyberg: Stateless vs Stateful Data Processing

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

article thumbnail

Processing Time Series Data With QuestDB and Apache Kafka

DZone

Apache Kafka is a battle-tested distributed stream-processing platform popular in the financial industry to handle mission-critical transactional workloads. Kafka’s ability to handle large volumes of real-time market data makes it a core infrastructure component for trading, risk management, and fraud detection.

article thumbnail

Batch vs. Real-Time Processing: Understanding the Differences

DZone

The decision between batch and real-time processing is a critical one, shaping the design, architecture, and success of our data pipelines. While both methods aim to extract valuable insights from data, they differ significantly in their execution, capabilities, and use cases. Key definitions can be summarized as follows:

article thumbnail

Dynatrace OpenPipeline: Stream processing data ingestion converges observability, security, and business data at massive scale for analytics and automation in context

Dynatrace

Organizations choose data-driven approaches to maximize the value of their data, achieve better business outcomes, and realize cost savings by improving their products, services, and processes. However, there are many obstacles and limitations along the way to becoming a data-driven organization.

Analytics 203