Cloudera

JANUARY 26, 2021

Use Case 1: NiFi pulling data from Kafka and pushing it to a file system (like HDFS). The Kafka coordinator, for the specified Consumer Group ID, will rebalance the existing topic partitions across the consumers from both HDF and CFM clusters. There should be no data ingested in HDF, only CFM.

Kafka 81

Kafka Hadoop Data Ingestion Utilities 81

Knowledge Hut

NOVEMBER 2, 2023

Top 10 Azure Data Engineering Project Ideas for Beginners For beginners looking to gain practical experience in Azure Data Engineering, here are 10 Azure Data engineer real time projects ideas that cover various aspects of data processing, storage, analysis, and visualization using Azure services: 1.

Data Engineer 52

Data Engineer Data Engineering Project Coding 52

Cloudera

SEPTEMBER 28, 2020

The customer also wanted to utilize the new features in CDP PvC Base like Apache Ranger for dynamic policies, Apache Atlas for lineage, comprehensive Kafka streaming services and Hive 3 features that are not available in legacy CDH versions. Lineage and chain of custody, advanced data discovery and business glossary. Kafka, SRM, SMM.

Cloud 132

Cloud Kafka Professional Services Metadata 132

Rockset

NOVEMBER 6, 2019

With event-driven architectures powered by systems like Apache Kafka becoming more prominent, there are now many applications in the modern software stack that make use of events and messages to operate effectively. Types of Event Data Applications emit events that correspond to important actions or state changes in their context.

Kafka 40

Kafka Data Lake SQL Hadoop 40

Knowledge Hut

APRIL 25, 2023

Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively. Data processing: Data engineers should know data processing frameworks like Apache Spark, Hadoop, or Kafka, which help process and analyze data at scale.

Data Engineer 52

Data Engineer Data Engineering Engineering Google Cloud 52

Pinterest Engineering

AUGUST 18, 2022

The HBase Ecosystem, though having various advantages like strong consistency at row level in high volume requests, flexible schema, low latency access to data, Hadoop integration, etc. In this blog post, we will first learn the various approaches considered for data migration with their trade offs.

Data Ingestion 52

Data Ingestion Hadoop Database Kafka 52

Cloudera

AUGUST 21, 2020

These platforms represent far more than just “Hadoop” . Over time, additional use cases and functions expanded from original EDW and Data Lake related functions to support increasing demands from the business. Streaming data analytics. . Data science & engineering. The only constant is change, however.

Hadoop 76

Hadoop Big Data Cloud Kafka 76

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

Silectis

JANUARY 3, 2021

Big Data Large volumes of structured or unstructured data. Big Data Processing In order to extract value or insights out of big data, one must first process it using big data processing software or frameworks, such as Hadoop. Big Query Google’s cloud data warehouse.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Monte Carlo

JUNE 2, 2024

These languages are used to write efficient, maintainable code and create scripts for automation and data processing. Databases and Data Warehousing: Engineers need in-depth knowledge of SQL (88%) and NoSQL databases (71%), as well as data warehousing solutions like Hadoop (61%).

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Monte Carlo

JUNE 2, 2024

These languages are used to write efficient, maintainable code and create scripts for automation and data processing. Databases and Data Warehousing: Engineers need in-depth knowledge of SQL (88%) and NoSQL databases (71%), as well as data warehousing solutions like Hadoop (61%).

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

ProjectPro

AUGUST 21, 2015

With the help of ProjectPro’s Hadoop Instructors, we have put together a detailed list of big data Hadoop interview questions based on the different components of the Hadoop Ecosystem such as MapReduce, Hive, HBase, Pig, YARN, Flume, Sqoop , HDFS, etc. What is the difference between Hadoop and Traditional RDBMS?

Hadoop 40

Hadoop Big Data Metadata Java 40

AltexSoft

MAY 14, 2021

Big Data analytics encompasses the processes of collecting, processing, filtering/cleansing, and analyzing extensive datasets so that organizations can use them to develop, grow, and produce better products. Big Data analytics processes and tools. Data ingestion. Apache Hadoop. Hadoop architecture layers.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

DECEMBER 7, 2021

However, you can also pull data from centralized data sources like data warehouses to transform data further and build ETL pipelines for training and evaluating AI agents. Processing: It is a data pipeline component that decides the data flow implementation.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Monte Carlo

APRIL 24, 2023

We continuously hear data professionals describe the advantage of the Snowflake platform as “it just works.” Snowpipe and other features makes Snowflake’s inclusion in this top data lake vendors list a no-brainer. Not to mention seamless integration with the Oracle ecosystem.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

ProjectPro

FEBRUARY 21, 2023

Hortonworks Data Engineering Certification The HDP Certified Developer (HDPCD) certification is another popular data engineering certification you can earn to build a successful career in this domain. Cloudera: You can take a Spark and Hadoop training course the platform provides. Candidates must register on www.examslocal.com.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? 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. Singer – An open source tool for moving data from a source to a destination.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Knowledge Hut

MAY 2, 2024

Features of Spark Speed : According to Apache, Spark can run applications on Hadoop cluster up to 100 times faster in memory and up to 10 times faster on disk. Spark streaming also has in-built connectors for Apache Kafka which comes very handy while developing Streaming applications. Spark streaming also supports Structure Streaming.

Scala 52

Scala Hospitality Healthcare Machine Learning 52

AltexSoft

AUGUST 29, 2023

Examples of unstructured data can range from sensor data in the industrial Internet of Things (IoT) applications, videos and audio streams, images, and social media content like tweets or Facebook posts. Data ingestion Data ingestion is the process of importing data into the data lake from various sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Confluent

SEPTEMBER 26, 2019

In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another data lake. However, Apache Kafka is more than just messaging. Some Kafka and Rockset users have also built real-time e-commerce applications , for example, using Rockset’s Java, Node.js

Kafka 21

Kafka SQL BI Hadoop 21

Knowledge Hut

SEPTEMBER 26, 2023

Born out of the minds behind Apache Spark, an open-source distributed computing framework, Databricks is designed to simplify and accelerate data processing, data engineering, machine learning, and collaborative analytics tasks. This flexibility allows organizations to ingest data from virtually anywhere.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Knowledge Hut

MARCH 28, 2024

Role Level: Intermediate Responsibilities Design and develop big data solutions using Azure services like Azure HDInsight, Azure Databricks, and Azure Data Lake Storage. Implement data ingestion, processing, and analysis pipelines for large-scale data sets.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Warehouse 52

ProjectPro

NOVEMBER 15, 2021

DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Apache Spark is also quite versatile, and it can run on a standalone cluster mode or Hadoop YARN , EC2, Mesos, Kubernetes, etc. CMAK is developed to help the Kafka community. To learn more about the recent updates and contribute: [link] 8.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

JANUARY 25, 2022

Features of PySpark Features that contribute to PySpark's immense popularity in the industry- Real-Time Computations PySpark emphasizes in-memory processing, which allows it to perform real-time computations on huge volumes of data. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency.

Big Data 52

Big Data Data Process Process Kafka 52

AltexSoft

SEPTEMBER 21, 2023

This remarkable efficiency is a game-changer compared to traditional batch processing engines like Hadoop , enabling real-time analytics and insights. With native integrations for major cloud platforms like AWS, Azure, and Google Cloud, sending data to Elastic Cloud is straightforward.

Engineering 52

Engineering NoSQL Programming Language Java 52

Confluent

OCTOBER 16, 2019

Trains are an excellent source of streaming data—their movements around the network are an unbounded series of events. Using this data, Apache Kafka ® and Confluent Platform can provide the foundations for both event-driven applications as well as an analytical platform. As with any real system, the data has “character.”

Kafka 19

Kafka Building Data Coding 19

phData: Data Engineering

JANUARY 3, 2022

In years past, some companies may have tried to create this report within Excel, having multiple business analysts and engineers contribute to data extraction and manipulation. Once the data has been collected from each system, a data engineer can determine how to optimally join the data sets.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Governance 52

phData: Data Engineering

JANUARY 3, 2022

phData Cloud Foundation is dedicated to machine learning and data analytics, with prebuilt stacks for a range of analytical tools, including AWS EMR, Airflow, AWS Redshift, AWS DMS, Snowflake, Databricks, Cloudera Hadoop, and more. The way you validate your data will be greatly influenced by your situation and architecture.

IT 52

IT AWS Software Engineering Software Engineer 52

ProjectPro

NOVEMBER 22, 2021

MapReduce Apache Spark Only batch-wise data processing is done using MapReduce. Apache Spark can handle data in both real-time and batch mode. The data is stored in HDFS (Hadoop Distributed File System), which takes a long time to retrieve. You can learn a lot by utilizing PySpark for data intake processes.

Hadoop 52

Hadoop Python Datasets Metadata 52

ProjectPro

MAY 31, 2021

The Apache Hadoop open source big data project ecosystem with tools such as Pig, Impala, Hive, Spark, Kafka Oozie, and HDFS can be used for storage and processing. Big Data Project using Hadoop with Source Code for Web Server Log Processing 5.

Big Data 52

Big Data Coding Project Hadoop 52

AltexSoft

MARCH 14, 2023

Additionally, this modularity can help prevent vendor lock-in, giving organizations more flexibility and control over their data stack. Many components of a modern data stack (such as Apache Airflow, Kafka, Spark, and others) are open-source and free. Offered as open-source with active support by communities.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Rockset

APRIL 15, 2022

Explosion in Streaming Data Before Kafka, Spark and Flink, streaming came in two flavors: Business Event Processing (BEP) and Complex Event Processing (CEP). Many (Kafka, Spark and Flink) were open source. It also prevents data bloat that would hamper storage efficiency and query speeds.

Analytics Application 52

Analytics Application Data Warehouse Kafka Raw Data 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. Let’s see what exactly Databricks has to offer.

Scala 64

Scala Data Lake BI Google Cloud 64