ProjectPro

JANUARY 25, 2022

PySpark is a handy tool for data scientists since it makes the process of converting prototype models into production-ready model workflows much more effortless. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency. RDD uses a key to partition data into smaller chunks.

Big Data 52

Big Data Data Process Process Kafka 52

Monte Carlo

OCTOBER 31, 2024

Let’s dive into the tools necessary to become an AI data engineer. Essential Skills for AI Data Engineers Expertise in Data Pipelines and ETL Processes A foundational skill for data engineers? The ability and skills to build scalable, automated data pipelines.

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 52

Cloudyard

JANUARY 21, 2025

Read Time: 6 Minute, 6 Second In modern data pipelines, handling data in various formats such as CSV, Parquet, and JSON is essential to ensure smooth data processing. However, one of the most common challenges faced by data engineers is the evolution of schemas as new data comes in.

Data Schemas 59

Data Schemas Data Pipeline Data Warehouse Data Storage 59

Data Engineering Weekly

FEBRUARY 2, 2025

Notably, the process includes an RL step to create a specialized reasoning model (R1-Zero) capable of excelling in reasoning tasks without labeled SFT data, highlighting advancements in training methodologies for AI models. link] Get Your Guide: From Snowflake to Databricks: Our cost-effective journey to a unified data warehouse.

Data Engineering 57

Data Engineering Data Engineer Engineering Data Lake 57

Monte Carlo

NOVEMBER 21, 2024

That’s where data pipeline design patterns come in. They’re basically architectural blueprints for moving and processing your data. So, why does choosing the right data pipeline design matter? In this guide, we’ll explore the patterns that can help you design data pipelines that actually work.

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

Cloudera

OCTOBER 1, 2020

Now, these companies are required to adhere to the principles of GDPR in order to legally transfer data to the US and process it. Which brings me to the third contributing factor, there is currently significant uncertainty around post-Brexit data regulation and the UK’s data-adequacy status.

Government 127

Government Cloud Data Storage Architecture 127

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

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

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 107

Metadata Healthcare Medical Data Storage 107

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

Striim

SEPTEMBER 11, 2024

With data volumes and sources rapidly increasing, optimizing how you collect, transform, and extract data is more crucial to stay competitive. That’s where real-time data, and stream processing can help. We’ll answer the question, “What are data pipelines?” Table of Contents What are Data Pipelines?

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

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

JULY 24, 2023

The future of SQL (Structured Query Language) is a scalding subject among professionals in the data-driven world. As data generation continues to skyrocket, the demand for real-time decision-making, data processing, and analysis increases. It is also integrable with other programming languages like Python and R.

Database 52

Database SQL Process NoSQL 52

Knowledge Hut

JANUARY 29, 2024

These servers are primarily responsible for data storage, management, and processing. With the increase in data production, data science has grown its popularity. Once big data is collected and stored by cloud computing, the factor of data science is put into this data.

Cloud Computing 98

Cloud Computing Data Science Cloud Amazon Web Services 98

Data Engineering Weekly

DECEMBER 2, 2023

[link] Amazon S3 Express One Zone is a high-performance, single-availability Zone storage class purpose-built to deliver consistent single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications. The combination of stream processing + OLAP storage like Pinot.

IT 85

IT BI AWS Kafka 85

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

Cloudera

MAY 30, 2024

One of the primary benefits of deploying AI and analytics within an open data lakehouse is the ability to centralize data from disparate sources into a single, cohesive repository. It provides flexibility in storing both raw and processed data, allowing organizations to adapt to changing data requirements and analytical needs.

Data Lake 74

Data Lake Data Warehouse Programming Language Data Ingestion 74

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

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. Here a small benchmark between some popular formats.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

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. Process Constraints.

Data Pipeline 98

Data Pipeline Data Data Analytics Architecture 98

Data Engineering Weekly

MARCH 8, 2023

Pathway is a Python framework for realtime data stream processing that handles updates for you. You can set up your processing pipeline, and Pathway will ingest the new streaming data points for you, sending you alerts in realtime. This portion of the data is called a window.

Process 52

Process Data Media Data Storage 52

Snowflake

SEPTEMBER 11, 2023

Data privacy regulations are sweeping across the globe, with some 71% of countries worldwide adopting data protection and privacy legislation. The European Union’s General Data Protection Regulation (GDPR) , one of the more well-known and far-reaching of these privacy regulations, went into effect on May 25, 2018. 1, Section 3.2,

Cloud 102

Cloud Electronics Government Data Governance 102

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

Snowflake

JUNE 5, 2024

Additionally, upon implementing robust data security controls and meeting regulatory requirements, businesses can confidently integrate AI while meeting compliance standards. Addressing a lack of in-house AI expertise and simplifying AI processes can make adoption easier. That’s where Snowflake comes in. Specifically, it offers: 1.

Coding 111

Coding Building Management Government 111

Netflix Tech

OCTOBER 19, 2020

Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices. The process started with manual pull of member account information that was part of the session.

Building 96

Building Transportation Java Metadata 96

Snowflake

JULY 10, 2023

Announced at Summit, we’ve recently added to Snowpark the ability to process files programmatically, with Python in public preview and Java generally available. Data engineers and data scientists can take advantage of Snowflake’s fast engine with secure access to open source libraries for processing images, video, audio, and more.

Unstructured Data 40

Unstructured Data Python Process Scala 40

Knowledge Hut

MAY 2, 2024

By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth. To store and process even only a fraction of this amount of data, we need Big Data frameworks as traditional Databases would not be able to store so much data nor traditional processing systems would be able to process this data quickly.

Hadoop 96

Hadoop Scala Datasets Java 96

Knowledge Hut

DECEMBER 22, 2023

They can categorize and cluster raw data using algorithms, spot hidden patterns and connections in it, and continually learn and improve over time. Hadoop Gigabytes to petabytes of data may be stored and processed effectively using the open-source framework known as Apache Hadoop. How to Become a Data Scientist in 2024?

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Sync Computing

JANUARY 27, 2025

Databricks clusters and AWS EC2 In todays landscape, big data, which is data too large to fit into a single node machine, is transformed and managed by clusters. In Databricks, clusters are groups of virtual computers that work together to process and analyze big data. But what are clusters? M6gd, C6gd).

AWS 52

AWS Data Lake Machine Learning Big Data 52

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

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Data Engineering Weekly

DECEMBER 16, 2024

Vector Search and Unstructured Data Processing Advancements in Search Architecture In 2024, organizations redefined search technology by adopting hybrid architectures that combine traditional keyword-based methods with advanced vector-based approaches.

Data Engineering 62

Data Engineering Data Engineer Engineering Unstructured Data 62

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 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. In this phase, data is collected from various sources.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Towards Data Science

OCTOBER 7, 2023

We dug deep into the early adopters’ strategies to learn how companies are putting this technology to use today — and what it takes for a data team to implement gen-AI at scale. For now, though, Databricks estimates that 80% of generative AI use cases in financial services are focused on streamlining processes to save time and resources.

Unstructured Data 98

Unstructured Data Finance SQL Database 98

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

AltexSoft

OCTOBER 30, 2021

Explaining the difference, especially when they both work with something intangible such as data , is difficult. If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. Here, data scientists are supported by data engineers.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98