Data Engineering Podcast

JULY 10, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Tired of deploying bad data? Tired of deploying bad data?

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Knowledge Hut

DECEMBER 26, 2023

Thus, to build a career in Data Science, you need to be familiar with how the business operates, its business model, strategies, problems, and challenges. Data Science Roles As Data Science is a broad field, you will find multiple different roles with different responsibilities.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

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

Data Engineering Podcast

OCTOBER 9, 2018

Summary One of the most complex aspects of managing data for analytical workloads is moving it from a transactional database into the data warehouse. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science.

PostgreSQL 100

PostgreSQL BI Data Warehouse Machine Learning 100

Knowledge Hut

JUNE 30, 2023

The book focuses on developing scalable and real-time data systems, covering data modeling, processing, and distributed systems. It covers popular technologies such as Apache Kafka, Apache Storm, and Apache Hadoop, giving users practical advice on developing and executing effective data pipelines.

Data Engineering 52

Data Engineering Data Engineer Engineering 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

Knowledge Hut

APRIL 25, 2023

Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Knowledge Hut

JUNE 20, 2023

Languages Python, SQL, Java, Scala R, C++, Java Script, and Python Tools Kafka, Tableau, Snowflake, etc. Skills A data engineer should have good programming and analytical skills with big data knowledge. Examples Pull daily tweets from the data warehouse hive spreading in multiple clusters.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Knowledge Hut

FEBRUARY 27, 2023

Engineers work with Data Scientists to help make the most of the data they collect and have deep knowledge of distributed systems and computer science. In large organizations, data engineers concentrate on analytical databases, operate data warehouses that span multiple databases, and are responsible for developing table schemas.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Knowledge Hut

JUNE 26, 2023

If you are struggling with Data Engineering projects for beginners, then Data Engineer Bootcamp is for you. Some simple beginner Data Engineer projects that might help you go forward professionally are provided below. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

ProjectPro

DECEMBER 7, 2021

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

JUNE 23, 2023

You must develop predictive models to help industries and businesses make data-driven decisions. Steps to Become a Data Engineer One excellent point is that you don’t need to enter the industry as a data engineer. You can start as a software administrator, a database analyst, or a business intelligence analyst.

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

Rockset

JANUARY 3, 2023

In the future, we expect to see a shift from companies using data pipelines to manage their data streaming needs to allowing this data to serve as a central nervous system so more people can derive smarter insights from it.” The predictions keep coming That’s all we got for real-time data predictions for 2023.

Data Warehouse 52

Data Warehouse Education Database Data 52

Cloudera

MAY 15, 2019

He is a successful architect of healthcare data warehouses, clinical and business intelligence tools, big data ecosystems, and a health information exchange. The Enterprise Data Cloud – A Healthcare Perspective. The analytics and data platform is powering different data needs, use cases, and growth.

Cloud 50

Cloud Hadoop Healthcare Big Data Ecosystem 50

Monte Carlo

JULY 7, 2022

They codify data needs in a data contract and schemas from consumers up front, and then deliver the data pre-modeled into the data warehouse. It’s becoming clear a data engineering movement is afoot as we have recently covered other organizations taking a similar approach: Convoy and Vimeo.

Data Warehouse 52

Data Warehouse Software Engineer Software Engineering Data 52

Rockset

JULY 30, 2019

Typically, transactional systems are online databases that are best suited for order entry, financial transactions, customer relationship management and retail sales, but they're not ideal for complex queries like determining how much of a particular product the business sold in a certain region this week and how that compares to last week.

BI 40

BI Medical Manufacturing Retail 40

Databand.ai

DECEMBER 13, 2022

We’d be remiss not to share that Joseph was a recent guest on Databand’s MAD Data Podcast , where he discussed ways to keep data systems from becoming unwieldy and shared tips for data teams to manage their data warehouses and keep data pipelines running reliably. You can also watch the video recording.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

ProjectPro

JUNE 9, 2021

Data Science looks into boosting the performance of a machine learning model. Data Engineering handles the entire data pipeline's optimization and efficiency for sourcing data from the data warehouse. It entails generating data visualizations and charts for analysis. Machine learning skills.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

AltexSoft

OCTOBER 21, 2022

Kafka can continue the list of brand names that became generic terms for the entire type of technology. Similar to Google in web browsing and Photoshop in image processing, it became a gold standard in data streaming, preferred by 70 percent of Fortune 500 companies. What is Kafka? What Kafka is used for.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

Knowledge Hut

DECEMBER 21, 2023

What is Data Engineering? Data engineering is the method to collect, process, validate and store data. It involves building and maintaining data pipelines, databases, and data warehouses. The purpose of data engineering is to analyze data and make decisions easier.

Data Engineering 52

Data Engineering Data Engineer Engineering MongoDB 52

Knowledge Hut

DECEMBER 26, 2023

Let us first get a clear understanding of why Data Science is important. What is the need for Data Science? If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

ProjectPro

JULY 8, 2015

World needs better Data Scientists Big data is making waves in the market for quite some time, there are several big data companies that have invested in Hadoop, NoSQL and data warehouses for collecting and storing big data.With open source tools like Apache Hadoop, there are organizations that have invested in millions for storing big data.

Hadoop 40

Hadoop R (Programming) Big Data NoSQL 40

Towards Data Science

DECEMBER 4, 2023

A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. Data Marts There is a thin line between Data Warehouses and Data Marts.

Data Engineering 80

Data Engineering Data Engineer Engineering Data Lake 80

Edureka

FEBRUARY 9, 2023

Ingest data into one or more Azure services, including Azure Data Lake, Azure Storage, Azure SQL, and Azure DW, and process the data in Azure Databricks. Develop pipelines in ADF that extract, transform, and load data from sources such as Azure SQL, Blob storage, Azure SQL Data Warehouse, write-back tools, and others.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Monte Carlo

APRIL 24, 2023

While we won’t get into the minutia of every consideration for every level of the data stack, it’s important to recall these five considerations as they’ll nonetheless steer the direction of our conversation. There are two primary types of raw data. The scale of data events depends entirely on the product.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Data Engineering Podcast

NOVEMBER 20, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Hevo]([link] Are you sick of repetitive, time-consuming ELT work?

Systems 130

Systems Metadata Data Pipeline MongoDB 130

ProjectPro

FEBRUARY 18, 2022

Why is ETL used in Data Science? It entails gathering data from numerous sources, converting it, and then storing it in a new single data warehouse. ETL pipelines help data scientists to prepare data for analytics and business intelligence. ETL stands for Extract, Transform, and Load.

Project 52

Project AWS Kafka Healthcare 52

Data Engineering Podcast

OCTOBER 5, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Start trusting your data with Monte Carlo today!

Engineering 100

Engineering MongoDB Kafka Data Lake 100

Cloudera

NOVEMBER 2, 2020

Users today are asking ever more from their data warehouse. As an example of this, in this post we look at Real Time Data Warehousing (RTDW), which is a category of use cases customers are building on Cloudera and which is becoming more and more common amongst our customers. What is Real Time Data Warehousing?

Data Warehouse 97

Data Warehouse Kafka Lambda Architecture Telecommunication 97

Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance.

Metadata 68

Metadata Data Governance Relational Database Government 68

AltexSoft

OCTOBER 30, 2021

Such visualizations as graphs and charts are typically prepared by data analysts or business analysts, though not every project has those people employed. Then, a data scientist uses complex business intelligence tools to present business insights to executives. Managing data and metadata.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

Monte Carlo

JUNE 14, 2023

The modern data stack era , roughly 2017 to present data, saw the widespread adoption of cloud computing and modern data repositories that decoupled storage from compute such as data warehouses, data lakes, and data lakehouses.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

AUGUST 24, 2021

Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis. Data Analytics: A data engineer works with different teams who will leverage that data for business solutions.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Rockset

FEBRUARY 24, 2022

Kinesis is modeled after Apache Kafka: both are general-purpose publish/subscribe messaging services, both are horizontally scalable, and both are high performance. Kafka is far more configurable on vectors like retention, performance and auto-scaling, but in turn requires a large team and weeks of setup. You need an on-prem solution.

AWS 52

AWS Amazon Web Services Kafka SQL 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52