Knowledge Hut

DECEMBER 7, 2023

Power BI is a powerful business intelligence tool developed by Microsoft that enables users to transform raw data into interactive and visually appealing insights. It caters to data analysis and visualization needs, aiding in making informed business decisions. What is Power BI?

BI 52

BI Raw Data Datasets Business Intelligence 52

Monte Carlo

MAY 17, 2021

Here’s how Ed Kent, Principal Developer at Auto Trader UK , the largest digital automotive marketplace in the UK and Ireland, is solving this problem by applying end-to-end Data Observability across their cloud warehouse, business intelligence systems, and beyond. “We Book a time to speak with us using the form below.

Data Ingestion 40

Data Ingestion Business Intelligence Data Pipeline Data 40

Knowledge Hut

MARCH 19, 2024

It provides high query performance and scalable storage for analytic load so that organizations can gain meaningful insights from their data. 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

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. This starts at the data source.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. This starts at the data source.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Databand.ai

JULY 17, 2023

The main objectives of DataOps include: Collaboration: Facilitating better communication between different teams involved in the data pipeline such as engineers, analysts, scientists, and business stakeholders. Better data observability equals better data quality.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

phData: Data Engineering

SEPTEMBER 19, 2023

Data warehouses have their own data modeling approaches that are typically more rigid than those for a data lake. Real-time Data Ingestion and Processing Data lakes can handle real-time data streams, making them ideal for use cases that require immediate data ingestion and processing.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Silectis

JANUARY 3, 2021

If you’re new to data engineering or are a practitioner of a related field, such as data science, or business intelligence, we thought it might be helpful to have a handy list of commonly used terms available for you to get up to speed. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Monte Carlo

MARCH 14, 2023

Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern data management workflows. Table of Contents What is Data Ingestion? Decision making would be slower and less accurate.

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

Netflix Tech

DECEMBER 18, 2020

I used my newfound Python and SQL skills to land an entry-level Business Intelligence Analyst position at a company called Big Ass Fans. Within a few months I’d picked up BI tools, predictive modeling, and data ingestion/ETL. As you grow in your career, you’ll provide impact to the business in ways you didn’t even expect.

Business Intelligence 55

Business Intelligence Data Science Recruitment Computer Science 55

Ascend.io

JANUARY 3, 2023

Here are some examples: The former United States Chief Data Scientist, DJ Patil, defined them in his book Data Jujitsu: The Art of Turning Data into Product as “a product that facilitates an end goal through the use of data”. Each widget on a dashboard can display a specific data product.

Data 52

Data Data Lake Business Intelligence Big Data 52

Maxime Beauchemin

JANUARY 20, 2017

I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. Instead, Facebook came to realize that the work we were doing transcended classic business intelligence. I wasn’t promoted or assigned to this new role.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Rockset

MAY 17, 2022

Complex SQL queries have long been commonplace in business intelligence (BI). And when systems such as Hadoop and Hive arrived, it married complex queries with big data for the first time. Flexible schemas that can adjust automatically based on the structure of the incoming streaming data.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data Warehouse 80

Data Warehouse Database-centric Metadata Cloud 80

Rockset

JULY 29, 2022

Google BigQuery BigQuery is Google’s data warehouse service and one of the first cloud data warehouses released to the public. The Snowpipe feature manages continuous data ingestion. However, this continuous streaming data isn’t available for a few minutes.

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

Knowledge Hut

JUNE 26, 2023

Finnhub API with Kafka for Real-Time Financial Market Data Pipeline Project Overview: The goal of this project is to construct a streaming data pipeline by making use of the real-time financial market data API provided by Finnhub.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Knowledge Hut

APRIL 25, 2023

With Power BI, data engineers can easily create interactive reports and dashboards that can be accessed from anywhere, on any device. Key features: Robust data visualization capabilities Seamless integration with Microsoft tools Easy-to-use interface 2. It is one of the most liked data engineering tools of the present day.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Monte Carlo

APRIL 24, 2023

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. This data ingestion process can be accomplished by either querying the source directly, using upstream systems to publish events, or some combination of the two.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

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

Edureka

FEBRUARY 9, 2023

As the demand for data engineers grows, having a well-written resume that stands out from the crowd is critical. Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions. It is also crucial to have experience with data ingestion and transformation.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

ProjectPro

JULY 22, 2016

Data lakes can store any type and amount of data and use it for their applications when required which is not possible with a conventional data warehouse. Data lakes eliminate the cost and data transformation complexity of data ingestion as they follow a “Schema on Read” approach.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

Data Engineering Podcast

NOVEMBER 16, 2020

Summary The core mission of data engineers is to provide the business with a way to ask and answer questions of their data. This often takes the form of business intelligence dashboards, machine learning models, or APIs on top of a cleaned and curated data set.

Data Management 100

Data Management Management BI Business Intelligence 100

DataKitchen

SEPTEMBER 21, 2023

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

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

Monte Carlo

JUNE 14, 2023

Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake. 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

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

Cloudera

JUNE 30, 2022

Now with Iceberg, CDP supports an open data lakehouse architecture that future-proofs our data platform for all our analytical workloads. We selected change data capture as our first use case on Iceberg.

Data Lake 84

Data Lake Business Intelligence Metadata Data Warehouse 84

Ascend.io

JUNE 8, 2023

Is it possible to treat data not just as a necessary operational output, but as a product that holds immense strategic value? Treating data as a product is more than a concept; it’s a paradigm shift that can significantly elevate the value that business intelligence and data-centric decision-making have on the business.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Cloudera

NOVEMBER 19, 2020

CDP works across private and hybrid cloud environments, and because it is built on open source capabilities, it is interoperable with a broad range of current and emerging analytic and business intelligence applications. Fraudulent Activity Detection.

Government 115

Government Machine Learning Algorithm Raw Data 115

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. While all three are about data acquisition, they have distinct differences.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Monte Carlo

FEBRUARY 15, 2023

A data warehouse is an online analytical processing system that stores vast amounts of data collected within a company’s ecosystem and acts as a single source of truth to enable downstream data consumers to perform business intelligence tasks, machine learning modeling, and more. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Cloudera

FEBRUARY 8, 2021

ECC will enrich the data collected and will make it available to be used in analysis and model creation later in the data lifecycle. Below is the entire set of steps in the data lifecycle, and each step in the lifecycle will be supported by a dedicated blog post(see Fig. 2 ECC data enrichment pipeline.

Data Pipeline 114

Data Pipeline Building Manufacturing Data Warehouse 114

Ascend.io

JULY 28, 2023

Data pipeline architecture is a framework that outlines the flow and management of data from its original source to its final destination within a system. This framework encompasses the steps of data ingestion, transformation, orchestration, and sharing. Let’s dig deeper: 1.

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

AltexSoft

SEPTEMBER 21, 2023

With native integrations for major cloud platforms like AWS, Azure, and Google Cloud, sending data to Elastic Cloud is straightforward. Its turn-key solutions further simplify data ingestion from multiple sources, including security systems and content repositories. Framework Programming The Good and the Bad of Node.js

Engineering 52

Engineering NoSQL Programming Language Java 52

ProjectPro

DECEMBER 6, 2016

Pentaho published a whitepaper titled “Hadoop and the Analytic Data Pipeline” that highlights the key categories which need to be focused on - Big Data Ingestion, Transformation, Analytics, Solutions. Source: [link] ) How Trifacta is helping data wranglers in Hadoop, the cloud, and beyond.Zdnet.com, November 4,2016.

Hadoop 52

Hadoop Data Lake BI Big Data 52

phData: Data Engineering

JANUARY 3, 2022

With that in place, data engineers can build data pipelines to allow data to flow out of the source systems. The result of this data pipeline is then stored in a separate location — generally in a highly available format for various business intelligence tools to query.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

AltexSoft

JULY 29, 2022

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. According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59