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!

Pipeline-centric 75

Pipeline-centric Engineering BI Google Cloud 75

Ascend.io

OCTOBER 20, 2023

Instead of burning through credits on inefficient SQL queries, Snowflake would rather you channel those resources into higher-impact projects. The Nuances of Snowflake Costing Snowflake’s pricing strategy is an exemplification of its user-centric approach: pay for what you use. No one wins with wasteful spending.

Pipeline-centric 52

Pipeline-centric IT Data Pipeline Bytes 52

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional responsible for designing, implementing, and managing data solutions using Microsoft's Azure cloud platform. They work with various Azure services and tools to build scalable, efficient, and reliable data pipelines, data storage solutions, and data processing systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

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. Write SQL queries without learning SQL? See how it works today.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

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.

Machine Learning 100

Machine Learning Pipeline-centric Database-centric MongoDB 100

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. With this 3rd platform generation, you have more real time data analytics and a cost reduction because it is easier to manage this infrastructure in the cloud thanks to managed services.

Technology 130

Technology Architecture Google Cloud Metadata 130

Ripple Engineering

JANUARY 29, 2024

  A lack of a centralized system makes building a single source of high-quality data difficult. Ripple Data Consumers query the data from the lake storage using the SQL strategy. It can lead to expensive, slow, and unmaintainable systems.

Database-centric 52

Database-centric Pipeline-centric NoSQL High Quality Data 52

Knowledge Hut

JUNE 20, 2023

In addition, they are responsible for developing pipelines that turn raw data into formats that data consumers can use easily. Languages Python, SQL, Java, Scala R, C++, Java Script, and Python Tools Kafka, Tableau, Snowflake, etc. The ML engineers act as a bridge between software engineering and data science.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

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.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Snowflake

JUNE 28, 2023

That’s why at our annual user conference, Snowflake Summit 2023 , we unveiled new features that further extend data programmability in Snowflake for their language of choice, without having to compromise on governance. To make it even easier to process data with Snowpark Python UDFs and Stored Procedures, we have added support for Python 3.9

Pipeline-centric 57

Pipeline-centric Programming Language Python Government 57

Snowflake

JUNE 28, 2023

At this year’s Summit, we are excited to announce a series of advancements to Snowpark runtimes and libraries, making the deployment and processing of non-SQL code in Snowflake even simpler, faster, and more secure. Snowpark — Set of libraries and runtimes for secure deployment and processing of non-SQL code on the Snowflake Data Cloud.

Python 52

Python Accessible Accessibility Pipeline-centric 52

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.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Data Engineering Weekly

JANUARY 8, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Pipelines for data in motion can quickly turn into DAG hell.

Data Engineering 40

Data Engineering Data Engineer Engineering Pipeline-centric 40

Monte Carlo

NOVEMBER 14, 2023

This is the world that data orchestration tools aim to create. Data orchestration tools minimize manual intervention by automating the movement of data within data pipelines. According to one Redditor on r/dataengineering, “Seems like 99/100 data engineering jobs mention Airflow.”

Pipeline-centric 40

Pipeline-centric Google Cloud Data Workflow Python 40

Knowledge Hut

SEPTEMBER 26, 2023

Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Designed to tackle the challenges of modern data management and analytics, Azure Synapse brings together the worlds of big data and data warehousing into a unified and seamlessly integrated platform.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

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.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

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. Interpreter / Compiler Interpreted Executed by a database engine, interpreting and executing SQL statements.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Knowledge Hut

JANUARY 30, 2024

Structured Data: Structured data sources, such as databases and spreadsheets, often require extraction to consolidate, transform, and make them suitable for analysis. This can involve SQL queries or ETL (Extract, Transform, Load) processes. call center recordings, chat logs) provides insights into customer preferences and needs.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

DataKitchen

NOVEMBER 4, 2021

DataOps works on top of your existing data, allowing you to use your favorite tools , whether you prefer SQL, Alteryx, Tableau, Looker or whatever else. DataOps is an open, secure platform that enables anyone granted permission to access data and update the processes and workflows that operate on data.

Process 98

Process Data Process Pharmaceutical Data Lake 98

Monte Carlo

JANUARY 23, 2024

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. But what does that mean for the roles of data engineers and data scientists going forward?

Pipeline-centric 52

Pipeline-centric Database-centric Metadata Unstructured Data 52

DataKitchen

FEBRUARY 21, 2023

As a result, a less senior team member was made responsible for modifying a production pipeline. ” Marcus will not fix his challenges by helping his team write SQL faster. To add fuel to the fire, a few weeks ago, one of the most talented people in the group left.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

AltexSoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. Data analysis.

Big Data 52

Big Data Data Process Process Hadoop 52

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.

Pipeline-centric 52

Pipeline-centric Data Warehouse Data Data Ingestion 52

phData: Data Engineering

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Databand.ai

DECEMBER 20, 2022

Follow Ken on LinkedIn 7) Michael Dillon Senior Business Intelligence Developer Michael is a Senior BI Developer at an English football club and the author of How to Get a Job in Data Analytics. Michael is certified in text mining and analytics, SQL, Python, Tableau, and more.

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 52

Monte Carlo

JULY 15, 2021

As data pipelines become increasingly complex, investing in a data quality solution is becoming an increasingly important priority for modern data teams. There are 4 key challenges, opportunities, and trade-offs when considering building or buying a data observability or data quality solution.

Building 40

Building Pipeline-centric Data Engineering Data Engineer 40

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.

Machine Learning 52

Machine Learning Engineering Pipeline-centric Database-centric 52