Data Engineering Podcast

JANUARY 14, 2017

Summary Do you wish that you could track the changes in your data the same way that you track the changes in your code? Pachyderm is a platform for building a data lake with a versioned file system. Interview with Daniel Whitenack Introduction How did you get started in the data engineering space?

Data Lake 100

Data Lake Raw Data Kafka Data Pipeline 100

Monte Carlo

AUGUST 31, 2023

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? In our opinion, data quality frequently gets a bad rep.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

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

Monte Carlo

JUNE 28, 2022

To help organizations realize the full potential of their data lake and lakehouse investments, Monte Carlo, the data observability leader, is proud to announce integrations with Delta Lake and Databricks’ Unity Catalog for full data observability coverage. billion in 2020 to 17.60 billion in 2020 to 17.60

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Ascend.io

SEPTEMBER 14, 2023

This feature enhances development speed, allowing data engineers to write and test scripts quickly — facilitating a more iterative and agile approach to data operations. Immediate Execution: Python code runs directly through the interpreter, eliminating the need for a separate compilation step.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Knowledge Hut

NOVEMBER 28, 2023

Let us take a look at the top technical skills that are required by a data engineer first: A. Technical Data Engineer Skills 1.Python Python is ubiquitous, which you can use in the backends, streamline data processing, learn how to build effective data architectures, and maintain large data systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 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. There are two primary types of raw data. But in many growing organizations, a combination of Fivetran and custom pipelines using Airflow will usually do the trick.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Knowledge Hut

OCTOBER 8, 2023

You must be proficient in NoSQL and SQL for data engineers to help with database management. Data pipeline design - It's where you extract raw data from different data sources and export it for analysis. Data engineers must design efficient pipelines for easy transfer of data.

AWS 52

AWS Data Engineering Data Engineer 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

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

FEBRUARY 16, 2023

Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. Your SQL skills as a data engineer are crucial for data modeling and analytics tasks. Making data accessible for querying is a common task for data engineers.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake? What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

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

Ascend.io

AUGUST 22, 2023

The demands of building, scaling, and maintaining data pipelines have grown increasingly complex and error-prone. Data engineers are now drowning in repetitive tasks, aspiring to drive data-backed decisions. Traditional approaches to building these pipelines have showcased their vulnerabilities.

Data Pipeline 52

Data Pipeline Raw Data Bytes Transportation 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

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. This can include tools for sharing data, code, and insights, as well as technologies for managing and coordinating data-related tasks and processes.

Machine Learning 52

Machine Learning Data Preparation Government Data Analytics 52

DareData

JULY 29, 2020

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines. What makes a good Data Pipeline?

Healthcare 52

Healthcare Data Pipeline Medical Pipeline-centric 52

Towards Data Science

FEBRUARY 9, 2024

As a result, here’s a short list of what inspired me to write an amendment to my original 2021 article : Scale Companies, big and small, are starting to reach levels of data scale previously reserved for Netflix, Uber, Spotify and other giants creating unique services with data.

Building 81

Building Transportation Data Lake Metadata 81

ThoughtSpot

SEPTEMBER 6, 2023

Those coveted insights live at the end of a process lovingly known as the data pipeline. The pathway from ETL to actionable analytics can often feel disconnected and cumbersome, leading to frustration for data teams and long wait times for business users. uptime and full data security compliance. And that’s all with 99.9%

Data Workflow 93

Data Workflow Raw Data Data Lake Business Intelligence 93

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

ProjectPro

NOVEMBER 14, 2015

Data Integration 3.Scalability Specialized Data Analytics 7.Streaming Specialized Data Analytics 7.Streaming Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization 1. To this group, we add a storage account and move the raw data.

Hadoop 52

Hadoop Project Big Data Healthcare 52

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. The ELT workflow. Scalability.

Process 52

Process Building Raw Data Data Lake 52

DataKitchen

NOVEMBER 29, 2021

We call this application of DataOps methods to the problem of AI bias “ equity as code ” because the tests that enforce equity are built into automated software applications that test, deploy and monitor the model 24/7. With data and tools increasingly in the cloud, data organizations are finding ways to accommodate remote work.

Data Lake 98

Data Lake Manufacturing Architecture Data Architecture 98

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

Monte Carlo

MARCH 14, 2023

Data from these sources are often ingested into a cloud-based data warehouse or data lake , where they can then be mined for information and insights. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley. Some data teams will leverage micro-batch strategies for time sensitive use cases.

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

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

AltexSoft

JANUARY 26, 2022

If we take the more traditional approach to data-related jobs used by larger companies, there are different specialists doing narrowly-focused tasks on different sides of the project. Data engineers build data pipelines and perform ETL — extract data from sources, transform it, and load it into a centralized repository like a data warehouse.

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

Databand.ai

JULY 28, 2022

In this very simplified example, we can see an ELT: Some pipeline tasks, probably running by Airflow , are scraping external data sources and collecting data from there. Those tasks are saving the extracted data in the data lake (or warehouse or lakehouse). This technique focuses directly on the data (vs.

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52

Monte Carlo

MAY 23, 2023

Business data assets at Assurance are loaded into the company’s lakehouse architecture through various methods, then stored in several data stores. The data team then uses tools like dbt and Airflow to refine, model and transform raw data into usable, query-able assets through Trino and Starburst.

Insurance 52

Insurance Business Intelligence Machine Learning Data 52

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

Confluent

JUNE 7, 2019

A big challenge is to support and manage multiple semantically enriched data models for the same underlying data, e.g., into a graph data model to trace value flow or into a MapReduce-compatible data model of the UTXO-based Bitcoin blockchain.

Accessible 92

Accessible Accessibility Kafka Scala 92

Towards Data Science

DECEMBER 23, 2023

We’ll start with the data engineering process, pulling data from an API and finally loading the transformed data into a data lake (represented by MinIO ). Reading and transforming data with PySpark : With our catch on board, we use PySpark, our compass, to navigate through this sea of data.

SQL 73

SQL Data Analytics Hadoop Raw Data 73

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

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

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. It is difficult to query the required unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JANUARY 31, 2022

It also offers a unique architecture that allows users to quickly build tables and begin querying data without administrative or DBA involvement. Snowflake is a cloud-based data platform that provides excellent manageability regarding data warehousing, data lakes, data analytics, etc. What Does Snowflake Do?

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

AltexSoft

SEPTEMBER 10, 2021

A newer way to integrate data into a centralized location is ELT. Consisting of the same steps as in ETL, ELT changes the sequence — it first extracts raw data from sources and loads it into a target source, where transformation happens as and when required. Key types of data integration. Types of data integration tools.

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Data Integration Data Consolidation Data Warehouse BI 52