Striim

MARCH 21, 2025

In-flight data processing reduces the time needed for data preparation as it delivers the data in a consumable form.

Data Warehouse 52

Data Warehouse MongoDB MySQL Hadoop 52

Knowledge Hut

APRIL 25, 2024

Big data is a term that refers to the massive volume of data that organizations generate every day. In the past, this data was too large and complex for traditional data processing tools to handle. There are a variety of big data processing technologies available, including Apache Hadoop, Apache Spark, and MongoDB.

Big Data 98

Big Data Technology Hadoop NoSQL 98

phData: Data Engineering

FEBRUARY 21, 2025

Prior to making a decision, an organization must consider the Total Cost of Ownership (TCO) for each potential data warehousing solution. On the other hand, cloud data warehouses can scale seamlessly. Vertical scaling refers to the increase in capability of existing computational resources, including CPU, RAM, or storage capacity.

Cloud 52

Cloud Data Warehouse Amazon Web Services Data Ingestion 52

AltexSoft

JULY 18, 2023

It allows data scientists to analyze large datasets and interactively run jobs on them from the R shell. Big data processing. Hadoop YARN : Often the preferred choice due to its scalability and seamless integration with Hadoop’s data storage systems, ideal for larger, distributed workloads.

Big Data 52

Big Data Data Process Process Hadoop 52

Data Engineering Weekly

MARCH 2, 2025

[link] Sneha Ghantasala: Slow Reads for S3 Files in Pandas & How to Optimize it DeepSeek’s Fire-Flyer File System (3FS) re-triggers the importance of an optimized file system for efficient data processing.

Data Engineering 62

Data Engineering Data Engineer Engineering Datasets 62

Data Engineering Weekly

FEBRUARY 2, 2025

I finally found a good critique that discusses its flaws, such as multi-hop architecture, inefficiencies, high costs, and difficulties maintaining data quality and reusability. The article advocates for a "shift left" approach to data processing, improving data accessibility, quality, and efficiency for operational and analytical use cases.

Data Engineering 57

Data Engineering Data Engineer Engineering Data Lake 57

Cloudera

OCTOBER 1, 2020

Such a status has yet to be granted and without which, data transfers between the UK and the EU will not be lawfully permitted post-December 31st 2020. Without an agreed legislative route to allow data storage and processing in the US and EU, the UK Government will be left with one option; storage and processing within the UK only.

Government 127

Government Cloud Data Storage Architecture 127

Snowflake

NOVEMBER 29, 2023

For example, the data storage systems and processing pipelines that capture information from genomic sequencing instruments are very different from those that capture the clinical characteristics of a patient from a site. A conceptual architecture illustrating this is shown in Figure 3.

Metadata 100

Metadata Healthcare Medical Data Storage 100

Monte Carlo

NOVEMBER 21, 2024

Think of it as the “slow and steady wins the race” approach to data processing. Stream Processing Pattern Now, imagine if instead of waiting to do laundry once a week, you had a magical washing machine that could clean each piece of clothing the moment it got dirty.

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

Striim

SEPTEMBER 11, 2024

Striim, for instance, facilitates the seamless integration of real-time streaming data from various sources, ensuring that it is continuously captured and delivered to big data storage targets. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

ProjectPro

JANUARY 25, 2022

PySpark Filter is used in conjunction with the Data Frame to filter data so that just the necessary data is used for processing, and the rest can be scarded. This allows for faster data processing since undesirable data is cleansed using the filter operation in a Data Frame.

Big Data 52

Big Data Data Process Process Kafka 52

Knowledge Hut

JANUARY 29, 2024

These servers are primarily responsible for data storage, management, and processing. Cloud Computing addresses this by offering scalable storage solutions, enabling Data Scientists to store and access vast datasets effortlessly. This process happens because of the increase in the growth of big data.

Cloud Computing 98

Cloud Computing Data Science Cloud Amazon Web Services 98

Data Engineering Weekly

DECEMBER 2, 2023

The paper discusses trade-offs among data freshness, resource cost, and query performance. Ref: [link] In the current state of the data infrastructure, we use a combination of multiple specialized data storage and processing engines to achieve this balance. What is Next?

IT 85

IT BI AWS Kafka 85

Data Engineering Podcast

AUGUST 17, 2020

What are the benefits of using matrices for data processing and domain modeling? What are the challenges that you have faced in storing and processing sparse matrices efficiently? How does the usage of matrices as the foundational primitive affect the way that users should think about data modeling?

Data Engineering 100

Data Engineering Data Engineer Engineering Database Design 100

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

Christophe Blefari

JANUARY 20, 2024

He wrote some years ago 3 articles defining data engineering field. Some concepts When doing data engineering you can touch a lot of different concepts. formats — This is a huge part of data engineering. Picking the right format for your data storage.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Pinterest Engineering

SEPTEMBER 26, 2023

While it is blessed with an abundance of data for training, it is also crucial to maintain a high data storage efficiency. Therefore, we adopted a hybrid data logging approach, with which the data is logged through both the backend service and the frontend clients. The process is captured in Figure 1.

Software Engineer 105

Software Engineer Software Engineering Machine Learning Datasets 105

Knowledge Hut

APRIL 23, 2024

The history of big data takes people on an astonishing journey of big data evolution, tracing the timeline of big data. The Emergence of Data Storage and Processing Technologies A data storage facility first appeared in the form of punch cards, developed by Basile Bouchon to facilitate pattern printing on textiles in looms.

Big Data 52

Big Data Amazon Web Services Cloud Computing Media 52

DataKitchen

APRIL 28, 2021

In Figure 1, the nodes could be sources of data, storage, internal/external applications, users – anything that accesses or relates to data. Data fabrics provide reusable services that span data integration, access, transformation, modeling, visualization, governance, and delivery.

Data Pipeline 98

Data Pipeline Data Data Analytics Architecture 98

Databand.ai

AUGUST 30, 2023

DataOps Architecture Legacy data architectures, which have been widely used for decades, are often characterized by their rigidity and complexity. These systems typically consist of siloed data storage and processing environments, with manual processes and limited collaboration between teams.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. The data is loaded as-is, without any transformation.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Knowledge Hut

JANUARY 18, 2024

IBM is one of the best companies to work for in Data Science. The platform allows not only data storage but also deep data processing by making use of Apache Hadoop. The CDP private cloud is a scalable data storage solution that can handle analytical and machine learning workloads.

Data Science 98

Data Science Amazon Web Services Big Data Finance 98

Monte Carlo

MAY 28, 2024

This involves connecting to multiple data sources, using extract, transform, load ( ETL ) processes to standardize the data, and using orchestration tools to manage the flow of data so that it’s continuously and reliably imported – and readily available for analysis and decision-making.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Snowflake

SEPTEMBER 11, 2023

I’d also like to offer some perspective on how best to handle issues such as a person’s right to rectification, data portability, data deletion and restriction of data processing. Once sensitive data is no longer available through Time Travel , it will be available in Fail Safe for seven days (non-configurable).

Cloud 95

Cloud Electronics Government Data Governance 95

Knowledge Hut

MAY 2, 2024

Most cutting-edge technology organizations like Netflix, Apple, Facebook, and Uber have massive Spark clusters for data processing and analytics. Data Processing MapReduce can only be used for batch processing where throughput is more important and latency can be compromised. Features of Spark 1.

Hadoop 96

Hadoop Scala Datasets Java 96

Striim

AUGUST 5, 2024

This episode promises invaluable insights into the shift from batch to real-time data processing, and the practical applications across multiple industries that make this transition not just beneficial but necessary. Explore the intricate challenges and groundbreaking innovations in data storage and streaming.

Kafka 52

Kafka Data Storage Architecture Data Architecture 52

Knowledge Hut

DECEMBER 28, 2023

An Azure Data Engineer is responsible for designing, implementing and managing data solutions on Microsoft Azure. The Azure Data Engineer certification imparts to them a deep understanding of data processing, storage and architecture. It makes us a versatile data professional.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. Any Azure Data Engineer must have experience with Azure’s data storage options, including Azure Cosmos DB, Azure Data Lake Storage, and Azure Blob Storage.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

DECEMBER 22, 2023

Hadoop Gigabytes to petabytes of data may be stored and processed effectively using the open-source framework known as Apache Hadoop. Hadoop enables the clustering of many computers to examine big datasets in parallel more quickly than a single powerful machine for data storage and processing. degrees.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Knowledge Hut

OCTOBER 6, 2023

I also find Amazon Athena useful because it allows me to do ad-hoc SQL searches on data stored in Amazon S3 without the need for time-consuming ETL procedures. My ability to get practical insights, thanks to AWS Data Analytics, makes it a crucial tool for businesses wanting to leverage data for profits.

AWS 52

AWS Certification Data Analytics IT 52

Netflix Tech

OCTOBER 19, 2020

This flexibility allows tracer libraries to record 100% traces in our mission-critical streaming microservices while collecting minimal traces from auxiliary systems like offline batch data processing. The next challenge was to stream large amounts of traces via a scalable data processing platform.

Building 95

Building Transportation Java Metadata 95

Cloudera

JUNE 26, 2023

That depends on the business use case, use case complexity, workflow complexity, and whether batch or streaming data is required. Use Nifi for ETL of streaming data, when real-time data processing is needed, or when data must flow from various sources rapidly and reliably.

ETL Tools 76

ETL Tools Programming Language Datasets Professional Services 76

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats. only structured data).

Data Management 52

Data Management Management Data Lake Data Warehouse 52

ProjectPro

JANUARY 19, 2022

Here are some role-specific skills you should consider to become an Azure data engineer- Most data storage and processing systems use programming languages. Data engineers must thoroughly understand programming languages such as Python, Java, or Scala. FAQs How hard is Azure Data Engineer Certification?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Storage 52

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Monte Carlo

JUNE 20, 2024

It consisted of three core components: Data connection: the connectivity to resources like Redshift, Snowflake, BigQuery, Databricks and many more (e.g., Data storage: any record-level or troubleshooting data (e.g., for data sampling) Data processing: the extraction and transformation collection engine (e.g.,

AWS 52

AWS Data Storage Cloud Google Cloud 52