Data Engineering Podcast

DECEMBER 16, 2019

Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics. Their web based transformation tool with built in collaboration features lets your analysts own the full lifecycle of data in your warehouse. Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

ProjectPro

FEBRUARY 16, 2023

ETL Architecture on AWS: Examining the Scalable Architecture for Data Transformation ETL Architecture on AWS typically consists of three components - Source Data Store A Data Transformation Layer Target Data Store Source Data Store The source data store is where raw data is stored before being transformed and loaded into the target data store.

AWS 52

AWS Data Management ETL Tools Management 52

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

ProjectPro

FEBRUARY 16, 2023

According to the 8,786 data professionals participating in Stack Overflow's survey, SQL is the most commonly-used language in data science. Despite the buzz surrounding NoSQL , Hadoop , and other big data technologies, SQL remains the most dominant language for data operations among all tech companies.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

ProjectPro

JUNE 9, 2021

Data Science- Definition Data Science is an interdisciplinary branch encompassing data engineering and many other fields. Data Science involves applying statistical techniques to raw data, just like data analysts, with the additional goal of building business solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

FEBRUARY 27, 2023

Data engineering builds data pipelines for core professionals like data scientists, consumers, and data-centric applications. Data engineering is also about creating algorithms to access raw data, considering the company's or client's goals.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer is someone proficient in a variety of programming languages and frameworks, such as Python, SQL, Scala, Hadoop, Spark, etc. One of the primary focuses of a Data Engineer's work is on the Hadoop data lakes. NoSQL databases are often implemented as a component of data pipelines.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is the role of a Data Engineer? Data scientists and data Analysts depend on data engineers to build these data pipelines.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

DataKitchen

JULY 27, 2023

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

JULY 12, 2021

We will now describe the difference between these three different career titles, so you get a better understanding of them: Data Engineer A data engineer is a person who builds architecture for data storage. They can store large amounts of data in data processing systems and convert raw data into a usable format.

Engineering 40

Engineering Deep Learning Software Engineer Software Engineering 40

Knowledge Hut

DECEMBER 22, 2023

They can categorize and cluster raw data using algorithms, spot hidden patterns and connections in it, and continually learn and improve over time. Hadoop Gigabytes to petabytes of data may be stored and processed effectively using the open-source framework known as Apache Hadoop.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

AltexSoft

JULY 29, 2022

Depending on how you measure it, the answer will be 11 million newspaper pages or… just one Hadoop cluster and one tech specialist who can move 4 terabytes of textual data to a new location in 24 hours. The Hadoop toy. So the first secret to Hadoop’s success seems clear — it’s cute. What is Hadoop?

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

AUGUST 24, 2021

Within no time, most of them are either data scientists already or have set a clear goal to become one. Nevertheless, that is not the only job in the data world. Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Knowledge Hut

MARCH 27, 2024

The collection of meaningful market data has become a critical component of maintaining consistency in businesses today. A company can make the right decision by organizing a massive amount of raw data with the right data analytic tool and a professional data analyst. Apache Spark. Apache Storm. Apache SAMOA.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

ProjectPro

SEPTEMBER 26, 2021

Big Data Engineers are professionals who handle large volumes of structured and unstructured data effectively. They are responsible for changing the design, development, and management of data pipelines while also managing the data sources for effective data collection.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

dbt Developer Hub

APRIL 16, 2023

Promote code to production in two week cycles instead of the previous quarter or semester-long cycles. With these principles in mind, we designed a highly scalable architecture, leveraging AWS-native services to extract and load the data into an S3-based data lake and a Redshift cluster Data Warehouse.

Cloud 59

Cloud Architecture Data Pipeline SQL 59

Knowledge Hut

SEPTEMBER 6, 2023

All the data science algorithms and concepts find their implementation in either Python or R. These languages have certain libraries designed specifically for data science specific tasks. Knowing the whereabouts of coding can alleviate the difficulty in understanding these use cases. Eligibility -.There

Data Science 97

Data Science Certification Business Analyst Machine Learning 97

AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Scalability.

Process 52

Process Building Raw Data Data Lake 52

Towards Data Science

DECEMBER 23, 2023

Table of contents Understanding the building blocks Setting up Docker Desktop Configuring MinIO Getting started with JupyterLab Data pipeline: The ETL process Analysing semi-structured data Cleanup of resources Understanding the building blocks This tutorial involves a range of technologies. I’m using Visual Studio Code.

SQL 71

SQL Data Analytics Hadoop Raw Data 71

Ascend.io

MAY 18, 2023

In this article, we’ll: Examine the evolution of the data stack Discuss the issues that have arisen from the modern data stack complexity Explore the next steps in the innovation cycle for data engineering The Evolution of the Data Stack Before we dive into the backstory of how we got here, let’s define what a data stack is.

Data Warehouse 52

Data Warehouse Data Pipeline Data Lake Data Engineering 52

ProjectPro

AUGUST 11, 2021

Is Snowflake a data lake or data warehouse? Is Hadoop a data lake or data warehouse? Since vast amounts of data is present in a data lake, it is ideal for tracking analytical performance and data integration. Recommended Reading: Is Hadoop Going To Replace Data Warehouse?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

LinkedIn Engineering

APRIL 4, 2023

It visualizes developer experience and happiness metrics describing key developer activities such code building, reviewing, publishing, as well as the sentiment towards the tools being used. Post-Commit CI Speed (P50 and P90) - measures how long it takes, in minutes, for each commit to get through the continuous integration (CI) pipeline.

MySQL 122

MySQL Datasets Software Engineer Software Engineering 122

ProjectPro

NOVEMBER 30, 2021

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

JANUARY 31, 2023

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Rockset

FEBRUARY 6, 2019

That meant a system that was sufficiently nimble and powerful to execute fast SQL queries on raw data, essentially performing any needed transformations as part of the query step, and not as part of a complex data pipeline. The existence of this serving layer obviates the need for complex data pipelines.

Lambda Architecture 40

Lambda Architecture Architecture MongoDB Kafka 40

ProjectPro

JUNE 24, 2021

Data Cleaning: To improve the data quality and filter the noisy, inaccurate, and irrelevant data for analysis, data cleaning is a key skill needed for all analytics job roles. Microsoft Excel: A successful Excel spreadsheet helps to organize raw data into a more readable format.

Data Analytics 40

Data Analytics Project Insurance Hadoop 40

ProjectPro

JULY 27, 2021

Data engineers and data scientists work very closely together, but there are some differences in their roles and responsibilities. Data Engineer Data scientist The primary role is to design and implement highly maintainable database management systems. A data warehouse can contain unstructured data too.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

APRIL 15, 2016

Preparing for a Hadoop job interview then this list of most commonly asked Apache Pig Interview questions and answers will help you ace your hadoop job interview in 2018. Research and thorough preparation can increase your probability of making it to the next step in any Hadoop job interview.

Hadoop 40

Hadoop Java Big Data SQL 40

ProjectPro

JULY 8, 2021

Kafka Streams and Kafka Connect were used to keep track of the threat of the COVID-19 virus and analyze the data for a more thorough response on local, state, and federal levels. Kafka is an integral part of Netflix’s real-time monitoring and event-processing pipeline.

Kafka 40

Kafka Architecture IT Big Data 40

ProjectPro

JANUARY 31, 2022

Snowflake provides data warehousing, processing, and analytical solutions that are significantly quicker, simpler to use, and more adaptable than traditional systems. Snowflake is not based on existing database systems or big data software platforms like Hadoop.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

MAY 31, 2021

To build a big data project, you should always adhere to a clearly defined workflow. Before starting any big data project, it is essential to become familiar with the fundamental processes and steps involved, from gathering raw data to creating a machine learning model to its effective implementation.

Big Data 52

Big Data Coding Project Hadoop 52

Confluent

OCTOBER 16, 2019

With tools like KSQL and Kafka Connect, the concept of streaming ETL is made accessible to a much wider audience of developers and data engineers. The platform shown in this article is built using just SQL and JSON configuration files—not a scrap of Java code in sight. Wrangling the data. train_id" : "161Y82MG06".

Kafka 19

Kafka Building Data Coding 19

Monte Carlo

MARCH 28, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Hell, the body of the Hadoop era isn’t even all that cold. Probably several times throughout our careers.

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Towards Data Science

APRIL 3, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. The body of the Hadoop era isn’t even all that cold. Probably several times throughout our careers.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

ProjectPro

MARCH 14, 2014

Every department of an organization including marketing, finance and HR are now getting direct access to their own data. This is creating a huge job opportunity and there is an urgent requirement for the professionals to master Big Data Hadoop skills. In 2015, big data has evolved beyond the hype.

Hadoop 40

Hadoop Big Data Data Mining Retail 40

AltexSoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization.

IT 59

IT Data Warehouse Data Governance Data Lake 59