Netflix Tech

NOVEMBER 20, 2023

IPS provides the incremental processing support with data accuracy, data freshness, and backfill for users and addresses many of the challenges in workflows. IPS enables users to continue to use the data processing patterns with minimal changes. A snapshot can contain data files from different partitions.

Process 84

Process Data Pipeline Datasets SQL 84

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Databand.ai

AUGUST 30, 2023

These experts will need to combine their expertise in data processing, storage, transformation, modeling, visualization, and machine learning algorithms, working together on a unified platform or toolset.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Knowledge Hut

SEPTEMBER 29, 2023

Microsoft Data Engineer Certification is one such certification which is most sought after by professionals. By combining data from various structured and unstructured data systems into structures, Microsoft Azure Data Engineers will be able to create analytics solutions.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

AltexSoft

NOVEMBER 7, 2022

DevOps tasks — for example, creating scheduled backups and restoring data from them. Airflow is especially useful for orchestrating Big Data workflows. Airflow is not a data processing tool by itself but rather an instrument to manage multiple components of data processing. Metadata database.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

Cloudera

AUGUST 21, 2020

The governance aspect is perhaps even more important and businesses need to be able to understand where the data comes from. Data lineage, personally identifiable information or PPI and metadata all fall under a broad data governance banner which is critically important in terms of what needs to be protected and mapped out.

Banking 59

Banking Kafka Cloud Storage Government 59

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

SEPTEMBER 21, 2023

The Elastic Stacks Elasticsearch is integral within analytics stacks, collaborating seamlessly with other tools developed by Elastic to manage the entire data workflow — from ingestion to visualization. Beats facilitate data movement from source to destination, which can be either Elasticsearch or Logstash, depending on the use case.

Engineering 52

Engineering NoSQL Programming Language Java 52

Databand.ai

DECEMBER 29, 2022

Follow Sudhir on LinkedIn 13) Benjamin Rogojan Data Science And Data Engineering Consultant at Acheron Analytics Benjamin is a data science and data engineering consultant with nearly a decade of experience working with companies like Healthentic, Facebook, and Acheron Analytics.

BI 52

BI Consulting Data Science Data Governance 52

Monte Carlo

JUNE 14, 2023

5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Ascend.io

MARCH 17, 2023

For data pipeline automation, these previous approaches have foundered on one important barrier: the need to scale. This can be done with unique digital fingerprints that map not just every snippet of data, but the data engineers’ code as well. Value Catching data problems in real-time avoids costly reruns and delays.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Ascend.io

MARCH 17, 2023

For data pipeline automation, these previous approaches have foundered on one important barrier: the need to scale. This can be done with unique digital fingerprints that map not just every snippet of data, but the data engineers’ code as well. Value Catching data problems in real-time avoids costly reruns and delays.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52