Data Engineering Podcast

AUGUST 13, 2022

In this episode founder Shayan Mohanty explains how he and his team are bringing software best practices and automation to the world of machine learning data preparation and how it allows data engineers to be involved in the process. Data stacks are becoming more and more complex. Data stacks are becoming more and more complex.

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Ascend.io

JUNE 21, 2023

Cost Savings : Insights derived from data products can directly lead to cost savings in the business. Risk Reduction : Data-centric decision-making can also contribute to risk reduction. In the case of data products, these are networks of data pipelines, which makes them an integral part of your modern operational machinery.

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Knowledge Hut

JUNE 20, 2023

Factors Data Engineer Machine Learning Definition Data engineers create, maintain, and optimize data infrastructure for data. In addition, they are responsible for developing pipelines that turn raw data into formats that data consumers can use easily. Assess the needs and goals of the business.

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Christophe Blefari

APRIL 8, 2023

At the same time Maxime Beauchemin wrote a post about Entity-Centric data modeling. In the recent years dbt simplified and revolutionised the tooling to create data models. This week I discovered SQLMesh , a all-in-one data pipelines tool. I hope he will fill the gaps. dbt, as of today, is the leading framework.

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Christophe Blefari

APRIL 8, 2023

At the same time Maxime Beauchemin wrote a post about Entity-Centric data modeling. In the recent years dbt simplified and revolutionised the tooling to create data models. This week I discovered SQLMesh , a all-in-one data pipelines tool. I hope he will fill the gaps. dbt, as of today, is the leading framework.

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Pipeline-centric Database-centric Algorithm Data 130

Data Engineering Weekly

JULY 2, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack Profiles takes the SaaS guesswork, and SQL grunt work out of building complete customer profiles, so you can quickly ship actionable, enriched data to every downstream team. See how it works today.

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François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. The next problem will be the diversity of these mini data platforms (because of the configuration) and you even go deeper in problems with managing different technologies or version.

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Databand.ai

JULY 19, 2023

This helps to enhance data quality, facilitate data governance, and enable regulatory compliance. Notable Data Lineage Tools and Solutions Let’s take a look at several notable data lineage tools that can improve the quality and efficiency of your data pipeline.

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Snowflake

JUNE 28, 2023

Streamlit gives data scientists and Python developers the ability to quickly turn data and models into interactive, enterprise-ready applications. Simplified streaming pipelines in Snowflake We are expanding our streaming capabilities with Dynamic Tables (public preview). We also announced the private preview of Snowflake CLI.

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The Modern Data Company

DECEMBER 29, 2022

This means moving beyond product-centric thinking to a data-driven customer experience model that’s consistent across all channels. Next, the wealth management industry is also shifting away from a product focus to a client-centric model. DataOS is the world’s first operating system.

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Pipeline Data Engineering

MARCH 2, 2022

Analytics Stacks for Startups Jan Katins, Senior IT Consultant/Data Engineer, kreuzwerker GmbH The stack should be relatively fast to implement (two weeks is possible), so you can quickly reap the benefits of having a data warehouse and BI Tooling in place or upload enriched data back to operational systems.

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Knowledge Hut

NOVEMBER 17, 2023

The demand for data-related professions, including data engineering, has indeed been on the rise due to the increasing importance of data-driven decision-making in various industries. Becoming an Azure Data Engineer in this data-centric landscape is a promising career choice.

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Knowledge Hut

FEBRUARY 27, 2023

Data engineering is all about building, designing, and optimizing systems for acquiring, storing, accessing, and analyzing data at scale. Data engineering builds data pipelines for core professionals like data scientists, consumers, and data-centric applications.

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Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. Open question: how to seed data in a staging environment? Test system with A/A test. Be adaptable.

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Data Engineering Weekly

APRIL 16, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make collecting data from every application, website, and SaaS platform easy, then activating it in your warehouse and business tools. Sign up free to test out the tool today.

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Data Engineering Data Engineer Engineering Pipeline-centric 52

DataKitchen

AUGUST 27, 2021

Without DataOps, companies can employ hundreds of data professionals and still struggle. The data pipelines must contend with a high level of complexity – over seventy data sources and a variety of cadences, including daily/weekly updates and builds. That’s the power of DataOps automation.

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Cloudera

NOVEMBER 15, 2021

Data Cascades are said to be pervasive, to lack immediate visibility, but to eventually impact the world in a negative manner. Related to the neglect of data quality, it has been observed that much of the efforts in AI have been model-centric, that is, mostly devoted to developing and improving models , given fixed data sets.

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DataKitchen

NOVEMBER 4, 2021

These limited-term databases can be generated as needed from automated recipes (orchestrated pipelines and qualification tests) stored and managed within the process hub. . The process hub capability of the DataKitchen Platform ensures that those processes that act upon data – the tests, the recipes – are shareable and manageable.

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Monte Carlo

AUGUST 2, 2022

As I begin to plot our data mesh implementation plan at Sanne Group, a leading asset management services provider, I can see why this idea that was originally introduced in 2019 has gained so much mindshare, but so few fully formed meshes nearly four years later. They will be staffed accordingly.

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Knowledge Hut

JULY 3, 2023

Real-time Data ingestion performs the utilization of data from various origins, does the data cleaning, validation, and preprocessing operations and at the end store it in the required format, either structured or unstructured. As real-time insights gain popularity, real-time data ingestion remains vital for companies worldwide.

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DataKitchen

FEBRUARY 21, 2023

As a result, a less senior team member was made responsible for modifying a production pipeline. When you architect for flexibility, quality, rapid deployment, and real-time data monitoring (in addition to your customer requirements), you move towards a DataOps-centric data engineering practice.

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Ascend.io

SEPTEMBER 14, 2023

Data engineers can find one for almost any need, from data extraction to complex transformations, ensuring that they’re not reinventing the wheel by writing code that’s already been written. In dynamic teams, where multiple stakeholders may interact with code or data pipelines, this readability becomes even more crucial.

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Monte Carlo

NOVEMBER 10, 2022

That means you have more API integrations and data pipelines that can fail. Often, by the time most marketing teams realize that their knight (the CDP) is a legacy data silo dressed in rusty armor, it’s too late. My guess is that their death will not be quick, but an agonizing slow descent into the oblivion of legacy technology.

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Knowledge Hut

JANUARY 30, 2024

Machine Data: For IoT applications, sensor data extraction is used to collect information from devices, machinery, or sensors, enabling real-time monitoring and analysis. Customer Interaction Data: In customer-centric industries, extracting data from customer interactions (e.g.,

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ProjectPro

DECEMBER 16, 2021

They need to know everything about the data and apply various mathematical and statistical tools to identify the most significant features using feature selection, feature engineering , feature transformation, etc.

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Ascend.io

MAY 31, 2023

In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Fast forward to the present day, and we now have data pipelines. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

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Data Engineering Podcast

APRIL 7, 2019

Summary How much time do you spend maintaining your data pipeline? This was a fascinating conversation with someone who has spent his entire career working on simplifying complex data problems. How does the data-centric approach of DataCoral differ from the way that other platforms think about processing information?

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Ascend.io

AUGUST 29, 2023

A star-studded baseball team is analogous to an optimized “end-to-end data pipeline” — both require strategy, precision, and skill to achieve success. Just as every play and position in baseball is key to a win, each component of a data pipeline is integral to effective data management.

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ProjectPro

JUNE 1, 2017

Datos IO has extended its on-premise and public cloud data protection to RDBMS and Hadoop distributions. RecoverX is described as app-centric and can back up applications data whilst being capable of recovering it at various granularity levels to enhance storage efficiency. now provides hadoop support.

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DareData

JULY 29, 2020

One paper suggests that there is a need for a re-orientation of the healthcare industry to be more "patient-centric". Furthermore, clean and accessible data, along with data driven automations, can assist medical professionals in taking this patient-centric approach by freeing them from some time-consuming processes.

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Cloudera

SEPTEMBER 17, 2020

To tackle these challenges, we’re thrilled to announce CDP Data Engineering (DE) , the only cloud-native service purpose-built for enterprise data engineering teams. Native Apache Airflow and robust APIs for orchestrating and automating job scheduling and delivering complex data pipelines anywhere.

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Data Pipeline Data Engineering Data Engineer Engineering 101

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.

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Monte Carlo

MARCH 28, 2024

In a nutshell, DataOps engineers are responsible not only for designing and building data pipelines, but iterating on them via automation and collaboration as well. So, does this mean you should choose DataOps engineering vs. data engineering when considering your next career move? What does a DataOps engineer do? It depends!

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Data Engineering Podcast

MAY 7, 2023

However, that's also something we're re-thinking with our warehouse-centric strategy. How does reverse ETL factor into the enrichment process for profile data? Rudderstack]([link] RudderStack provides all your customer data pipelines in one platform. Let us know if you have opinions there!

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Ascend.io

JUNE 8, 2023

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. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

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Snowflake

JUNE 28, 2023

Snowpark is our secure deployment and processing of non-SQL code, consisting of two layers: Familiar Client Side Libraries – Snowpark brings deeply integrated, DataFrame-style programming and OSS compatible APIs to the languages data practitioners like to use. Previously, tasks could be executed as quickly as 1-minute.

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Ripple Engineering

JANUARY 29, 2024

  A lack of a centralized system makes building a single source of high-quality data difficult. The key aspect of any business-centric team in delivering products and features is to make critical decisions on ensuring low latency, high throughput, cost-effective storage, and highly efficient infrastructure.

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