ProjectPro

JANUARY 25, 2022

Here’s What You Need to Know About PySpark This blog will take you through the basics of PySpark, the PySpark architecture, and a few popular PySpark libraries , among other things. Finally, you'll find a list of PySpark projects to help you gain hands-on experience and land an ideal job in Data Science or Big Data.

Big Data 52

Big Data Data Process Process Kafka 52

Cloudyard

APRIL 4, 2024

However, the volume of daily transaction data poses challenges in effectively segmenting customers and optimizing engagement. This blog post explores how Snowpark, a powerful tool for data processing within Snowflake, can be used to perform RFM segmentation and unlock actionable customer insights.

Retail 40

Retail Data Ingestion Metadata Datasets 40

Data Engineering Weekly

NOVEMBER 20, 2022

Google AI: The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation Google published Data Cards , a dataset documentation framework aimed at increasing transparency across dataset lifecycles. link] The short YouTube video gives a nice overview of the Data Cards.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Databand.ai

JULY 17, 2023

Aim to automate processes: Automation is a key aspect of both DataOps and MLOps as it helps streamline workflows, reduce errors, increase efficiency, and ensure consistency across projects. However, if machine learning models are at the core of your business operations, MLOps will provide better support.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

ProjectPro

FEBRUARY 6, 2023

Data engineers often use Google Cloud Pub/Sub to design asynchronous workflows, publish event notifications, and stream data from several processes or devices. This blog provides an overview of Google Cloud Pub/Sub that will help you understand the framework and its suitable use cases for your data engineering projects.

Google Cloud 52

Google Cloud Cloud Cloud Storage Data Ingestion 52

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Ascend.io

JUNE 8, 2023

However, transforming data into a product so that it can deliver outsized business value requires more than just a mission statement; it requires a solid foundation of technical capabilities and a truly data-centric culture. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Knowledge Hut

MARCH 19, 2024

I will explore the top 10 AWS applications and their use cases in this blog. Lambda usage includes real-time data processing, communication with IoT devices, and execution of automated tasks. It makes real-time streaming data collection, processing, and analytics possible for timely insight and decision-making for businesses.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

Cloudera

JANUARY 20, 2021

The missing chapter is not about point solutions or the maturity journey of use cases, the missing chapter is about the data, it’s always been about the data, and most importantly the journey data weaves from edge to artificial intelligence insight. . Conclusion.

Manufacturing 105

Manufacturing Data Warehouse Kafka Retail 105

Ascend.io

JUNE 6, 2023

In this blog post, we delve deep into the factors that contribute to how Ascend runs in the cloud, focusing on the primary areas that contribute to costs in this context: storage, compute, networking, and retries. Anytime data processing encounters a failure for a variety of reasons, retries commonly resolve the problem.

Cloud 52

Cloud Data Pipeline Data Ingestion Cloud Storage 52

LinkedIn Engineering

DECEMBER 12, 2023

We say that an algorithm is differentially private if any result of the algorithm cannot depend too much on any single data record in a dataset. Hence, differential privacy provides uncertainty for whether or not an individual data record is in the dataset.

Algorithm 77

Algorithm Metadata SQL Datasets 77

ProjectPro

AUGUST 24, 2021

Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project. And, out of these professions, this blog will discuss the data engineering job role.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Confluent

FEBRUARY 6, 2019

The blog posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable and mission-critical nervous system. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Python Kafka Java 107

ProjectPro

JANUARY 31, 2023

If you're looking to break into the exciting field of big data or advance your big data career, being well-prepared for big data interview questions is essential. Get ready to expand your knowledge and take your big data career to the next level! But the concern is - how do you become a big data professional?

Big Data 52

Big Data Hadoop AWS Relational Database 52

DareData

JULY 5, 2023

In this blog post, we aim to share practical insights and techniques based on our real-world experience in developing data lake infrastructures for our clients - let's start! Typically, it is advisable to retain the data in its original, unaltered format when transferring it from any source to the data lake layer.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

AWS 98

AWS Scala Metadata Data Lake 98

ProjectPro

FEBRUARY 21, 2023

With so many data engineering certifications available , choosing the right one can be a daunting task. There are over 133K data engineer job openings in the US, but how will you stand out in such a crowded job market? Why Are Data Engineering Skills In Demand? Don’t worry!

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Rockset

MAY 17, 2022

This is the fourth post in a series by Rockset's CTO Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics. We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them! Instant scaleup of data writes or queries to handle bursts of data.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Netflix Tech

OCTOBER 19, 2020

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. An additional implication of a lenient sampling policy is the need for scalable stream processing and storage infrastructure fleets to handle increased data volume. —?which is difficult when troubleshooting distributed systems.

Building 93

Building Transportation Metadata Java 93

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. A pipeline may include filtering, normalizing, and data consolidation to provide desired data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

AltexSoft

MAY 14, 2021

As you now know the key characteristics, it gets clear that not all data can be referred to as Big Data. What is Big Data analytics? Big Data analytics is the process of finding patterns, trends, and relationships in massive datasets that can’t be discovered with traditional data management techniques and tools.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

NOVEMBER 15, 2021

Table of Contents 20 Open Source Big Data Projects To Contribute How to Contribute to Open Source Big Data Projects? 20 Open Source Big Data Projects To Contribute There are thousands of open-source projects in action today. This blog will walk through the most popular and fascinating open source big data projects.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

MAY 31, 2021

Ace your big data interview by adding some unique and exciting Big Data projects to your portfolio. This blog lists over 20 big data projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies.

Big Data 52

Big Data Coding Project Hadoop 52

ProjectPro

NOVEMBER 22, 2021

What's the difference between an RDD, a DataFrame, and a DataSet? RDDs contain all datasets and dataframes. If a similar arrangement of data needs to be calculated again, RDDs can be efficiently reserved. It's useful when you need to do low-level transformations, operations, and control on a dataset. Output- Q13.

Hadoop 52

Hadoop Python Datasets Metadata 52

Confluent

SEPTEMBER 26, 2019

In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another data lake. ® , Go, and Python SDKs where an application can use SQL to query raw data coming from Kafka through an API (but that is a topic for another blog). Joining with other datasets.

Kafka 21

Kafka BI SQL Datasets 21

Cloudera

SEPTEMBER 7, 2023

Given the complexity of the datasets used to train AI systems, and factoring in the known tendency of generative AI systems to invent non-factual information, this is no small task. By embedding intelligence at the data platform-level, it becomes possible to accelerate the pace at which financial institutions can operationalize AI solutions.

Insurance 79

Insurance Finance Portfolio Government 79

Cloudera

FEBRUARY 8, 2021

This is part 2 in this blog series. You can read part 1, here: Digital Transformation is a Data Journey From Edge to Insight. The first blog introduced a mock connected vehicle manufacturing company, The Electric Car Company (ECC), to illustrate the manufacturing data path through the data lifecycle.

Data Pipeline 114

Data Pipeline Building Manufacturing Data Warehouse 114

ProjectPro

OCTOBER 20, 2021

If you are unsure, be vocal about your thought process and the way you are thinking – take inspiration from the examples below and explain the answer to the interviewer through your learnings and experiences from data science and machine learning projects. Data: Data Engineering Pipelines Data is everything.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

Confluent

OCTOBER 10, 2019

Apache Kafka ® and its surrounding ecosystem, which includes Kafka Connect, Kafka Streams, and KSQL, have become the technology of choice for integrating and processing these kinds of datasets. MQTT Proxy for data ingestion without an MQTT broker. But that doesn’t move much.

Kafka 20

Kafka Google Cloud Architecture Machine Learning 20

phData: Data Engineering

JANUARY 3, 2022

To guarantee that the latest version of a table was used when the views and materialized views were created, we used a templating library (Jinja) that would reference our configuration files for each dataset/table we had and programmatically generate the required data definition language (DDL).

IT 52

IT AWS Software Engineer Software Engineering 52

Cloudera

JANUARY 22, 2019

Forrester describes Big Data Fabric as, “A unified, trusted, and comprehensive view of business data produced by orchestrating data sources automatically, intelligently, and securely, then preparing and processing them in big data platforms such as Hadoop and Apache Spark, data lakes, in-memory, and NoSQL.”.

Big Data 44

Big Data NoSQL Data Lake Hadoop 44

U-Next

JUNE 29, 2022

However, the benefits might be game-changing: a well-designed big data pipeline can significantly differentiate a company. In this blog, we’ll go over elements of big data , the big data environment as a whole, big data infrastructures, and some valuable tools for getting it all done. Consumption .

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

Cloudera

JUNE 16, 2022

Use cases like fraud detection, network threat analysis, manufacturing intelligence, commerce optimization, real-time offers, instantaneous loan approvals, and more are now possible by moving the data processing components up the stream to address these real-time needs. . Faster data ingestion: streaming ingestion pipelines.

Kafka 84

Kafka Manufacturing Data Lake SQL 84

phData: Data Engineering

JULY 18, 2023

First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse. Central Source of Truth for Analytics A Cloud Data Warehouse (CDW) is a type of database that provides analytical data processing and storage capabilities within a cloud-based infrastructure.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52