Analytics Vidhya

MARCH 7, 2023

Introduction Apache Flume is a tool/service/data ingestion mechanism for gathering, aggregating, and delivering huge amounts of streaming data from diverse sources, such as log files, events, and so on, to centralized data storage. Flume is a tool that is very dependable, distributed, and customizable.

Data Ingestion 216

Data Ingestion Data Storage Hadoop Data 216

Striim

NOVEMBER 13, 2023

Introduction In the fast-evolving world of data integration, Striim’s collaboration with Snowflake stands as a beacon of innovation and efficiency. Striim’s integration with Snowpipe Streaming represents a significant advancement in real-time data ingestion into Snowflake.

Data Ingestion 59

Data Ingestion Utilities Data Integration Data 59

Rock the JVM

JANUARY 21, 2023

Introduction Apache Kafka is a well-known event streaming platform used in many organizations worldwide. It is used as the backbone of many data infrastructures, thus it’s important to understand how to use it efficiently. The code samples are written in Kotlin, but the implementation should be easy to port in Java or Scala.

Kafka 69

Kafka Java Scala Coding 69

Knowledge Hut

APRIL 25, 2023

An end-to-end Data Science pipeline starts from business discussion to delivering the product to the customers. One of the key components of this pipeline is Data ingestion. It helps in integrating data from multiple sources such as IoT, SaaS, on-premises, etc., What is Data Ingestion?

Data Ingestion 52

Data Ingestion Lambda Architecture Raw Data Data Science 52

Lyft Engineering

NOVEMBER 29, 2023

Druid at Lyft Apache Druid is an in-memory, columnar, distributed, open-source data store designed for sub-second queries on real-time and historical data. Druid enables low latency (real-time) data ingestion, flexible data exploration and fast data aggregation resulting in sub-second query latencies.

Kafka 105

Kafka Data Ingestion Datasets Architecture 105

Cloudera

APRIL 20, 2021

The developers must understand lower-level languages like Java and Scala and be familiar with the streaming APIs. A modern streaming architecture consists of critical components that provide data ingestion, security and governance, and real-time analytics. What is modern streaming architecture?

Kafka 100

Kafka Hospitality Retail Data Ingestion 100

Rockset

OCTOBER 11, 2022

In this blog, we’ll compare and contrast how Elasticsearch and Rockset handle data ingestion as well as provide practical techniques for using these systems for real-time analytics. Or, they can periodically scan their relational database to get access to the most up to date records and reindex the data in Elasticsearch.

Data Ingestion 52

Data Ingestion Kafka Relational Database PostgreSQL 52

LinkedIn Engineering

OCTOBER 19, 2023

To enable the ingestion and real-time processing of enormous volumes of data, LinkedIn built a custom stream processing ecosystem largely with tools developed in-house (and subsequently open-sourced). In 2010, they introduced Apache Kafka , a pivotal Big Data ingestion backbone for LinkedIn’s real-time infrastructure.

Process 119

Process Lambda Architecture Kafka Machine Learning 119

Data Engineering Podcast

SEPTEMBER 11, 2022

The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Data Pipeline 130

Data Pipeline Building MongoDB MySQL 130

Confluent

JULY 3, 2019

Twitter represents the default source for most event streaming examples, and it’s particularly useful in our case because it contains high-volume event streaming data with easily identifiable keywords that can be used to filter for relevant topics. Ingesting Twitter data. connector.state]. Transfermarkt. The Guardian.

Data Analysis 21

Data Analysis Kafka Datasets Java 21

Data Engineering Podcast

AUGUST 21, 2022

The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Lambda Architecture 100

Lambda Architecture MongoDB MySQL Scala 100

Christophe Blefari

JANUARY 20, 2024

The Rise of the Data Engineer The Downfall of the Data Engineer Functional Data Engineering — a modern paradigm for batch data processing There is a global consensus stating that you need to master a programming language (Python or Java based) and SQL in order to be self-sufficient. This is not.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Data Engineering Podcast

AUGUST 28, 2022

The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Building 100

Building MongoDB MySQL Scala 100

Data Engineering Podcast

SEPTEMBER 25, 2022

The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Food 100

Food MongoDB MySQL Scala 100

Cloudera

FEBRUARY 9, 2021

If you are a database administrator or developer, you can start writing queries right-away using Apache Phoenix without having to wrangle Java code. . To store and access data in the operational database, you can do one of the following: Use native Apache HBase client APIs to interact with data in HBase: Use the HBase APIs for Java.

Database 105

Database Java SQL Data Ingestion 105

Data Engineering Podcast

AUGUST 6, 2022

The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

Cloudera

JUNE 28, 2022

In a previous blog of this series, Turning Streams Into Data Products , we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. The use case. The streaming SQL job also saves the fraud detections to the Kudu database.

Process 91

Process Kafka SQL Machine Learning 91

Cloudera

JANUARY 11, 2021

MiNiFi comes in two versions: C++ and Java. The MiNiFi Java option is a lightweight single node instance, a headless version of NiFi without the user interface nor the clustering capabilities. Still, it requires Java to be available on the host. Still, it requires Java to be available on the host.

Kafka 63

Kafka Java Data Cleanse Datasets 63

Confluent

FEBRUARY 21, 2019

HELK is a free threat hunting platform built on various components including the Elastic stack, Apache Kafka ® and Apache Spark. WHERE PARENT_PROCESS_PATH LIKE '%WmiPrvSE.exe%'; The results of the KSQL query can be written to a Kafka topic, which in turn can drive real-time monitoring or alerting dashboards and applications.

Process 83

Process Kafka SQL Datasets 83

Striim

JULY 31, 2024

Why Striim Stands Out As detailed in the GigaOm Radar Report, Striim’s unified data integration and streaming service platform excels due to its distributed, in-memory architecture that extensively utilizes SQL for essential operations such as transforming, filtering, enriching, and aggregating data.

Aggregated Data 52

Aggregated Data Data Ingestion Java Kafka 52

ProjectPro

DECEMBER 28, 2021

Apache Spark Streaming Use Cases Spark Streaming Architecture: Discretized Streams Spark Streaming Example in Java Spark Streaming vs. Structured Streaming Spark Streaming Structured Streaming What is Kafka Streaming? Kafka Stream vs. Spark Streaming What is Spark streaming? Table of Contents What is Spark streaming?

Architecture 52

Architecture Kafka Java Scala 52

Snowflake

AUGUST 16, 2023

That’s why we built Snowpipe Streaming, now generally available to handle row-set data ingestion. The new Kafka connector, built with Snowpipe Streaming , now supports schema detection and evolution. Snowpipe streaming supports both database replication and group-based replication. Learn more here.

Transportation 52

Transportation Scala Kafka Data Lake 52

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 Engineering 52

Data Engineering Data Engineer Coding Project 52

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 88

Engineering Raw Data Data Science Machine Learning 88

AltexSoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. What is Hadoop? Hadoop ecosystem evolvement.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

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 Engineering 52

Data Engineering Data Engineer Project Coding 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

Knowledge Hut

APRIL 25, 2023

Here are some essential skills for data engineers when working with data engineering tools. Strong programming skills: Data engineers should have a good grasp of programming languages like Python, Java, or Scala, which are commonly used in data engineering.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Rockset

MARCH 27, 2023

Stream processing tools manipulate streaming data as it flows through a streaming data platform (Kafka being one of the most popular options, but there are others). This processing happens incrementally, as the streaming data arrives. It was developed by the Apache Software Foundation and is written in Java and Scala.

Database 52

Database Process Scala SQL 52

Knowledge Hut

MAY 2, 2024

As per Apache, “ Apache Spark is a unified analytics engine for large-scale data processing ” Spark is a cluster computing framework, somewhat similar to MapReduce but has a lot more capabilities, features, speed and provides APIs for developers in many languages like Scala, Python, Java and R.

Scala 52

Scala Hospitality Healthcare Machine Learning 52

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

AltexSoft

SEPTEMBER 21, 2023

It is developed in Java and built upon the highly reputable Apache Lucene library. With native integrations for major cloud platforms like AWS, Azure, and Google Cloud, sending data to Elastic Cloud is straightforward. This means that Elasticsearch can be easily integrated into different modern data stacks.

Engineering 52

Engineering NoSQL Programming Language Java 52

ProjectPro

NOVEMBER 15, 2021

It even allows you to build a program that defines the data pipeline using open-source Beam SDKs (Software Development Kits) in any three programming languages: Java, Python, and Go. CMAK Source: Github CMAK stands for Cluster Manager for Apache Kafka , previously known as Kafka Manager, is a tool for managing Apache Kafka clusters.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

FEBRUARY 21, 2023

Proficiency in data ingestion, including the ability to import and export data between your cluster and external relational database management systems and ingest real-time and near-real-time (NRT) streaming data into HDFS. big data and ETL tools, etc. PREVIOUS NEXT <

Certification 52

Certification Data Engineering Data Engineer Engineering 52

ProjectPro

AUGUST 21, 2015

Hadoop Framework works on the following two core components- 1)HDFS – Hadoop Distributed File System is the java based file system for scalable and reliable storage of large datasets. Data in HDFS is stored in the form of blocks and it operates on the Master-Slave Architecture. How Sqoop can be used in a Java program?

Hadoop 40

Hadoop Big Data Metadata Java 40

phData: Data Engineering

JANUARY 3, 2022

A common example of this would be taking a Java project and building that into a jar file. This jar file can then be executed by the Java runtime on any server with a compatible Java version. The way you validate your data will be greatly influenced by your situation and architecture.

IT 52

IT AWS Software Engineer Software Engineering 52

ProjectPro

NOVEMBER 22, 2021

The core engine for large-scale distributed and parallel data processing is SparkCore. The distributed execution engine in the Spark core provides APIs in Java, Python, and Scala for constructing distributed ETL applications. The cache() function or the persist() method with proper persistence settings can be used to cache data.

Hadoop 52

Hadoop Python Datasets Metadata 52