Knowledge Hut

JUNE 20, 2023

Let's find out the differences between a data scientist and a machine learning engineer below to make an informative decision. Data Engineer vs Machine Learning Engineer While there are similarities between a data engineer and a machine learning engineer, both play a key role in the technological world.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Data Engineering Weekly

APRIL 2, 2023

Meta: Presto - A Decade of SQL Analytics at Meta Presto and Kafka are the two systems that greatly impacted data infrastructure in the last decade. The cluster split approach to store real-time, protected, and archive tweets is an excellent reference model for designing enterprise search engines.

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

Knowledge Hut

FEBRUARY 27, 2023

A data engineer is a key member of an enterprise data analytics team and is responsible for handling, leading, optimizing, evaluating, and monitoring the acquisition, storage, and distribution of data across the enterprise. Data Engineers indulge in the whole data process, from data management to analysis.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Monte Carlo

MAY 30, 2024

Generative AI has the potential to transform your business and your data engineering team, but only when it’s done right. So how can your data team actually drive value with your LLM or GenAI initiative? But when it’s done right, RAG can add an incredible amount of value to AI-powered data products. What is Fine Tuning?

Pipeline-centric 52

Pipeline-centric Database-centric Datasets Data Pipeline 52

Maxime Beauchemin

JANUARY 20, 2017

I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. We were data engineers! Data Engineering? Data science as a discipline was going through its adolescence of self-affirming and defining itself. We were pioneers.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Grouparoo

OCTOBER 6, 2021

It was a fun experience and I think we made a good choice by picking 97 Things Every Data Engineer Should Know. 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.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Christophe Blefari

DECEMBER 19, 2022

But this article is not about the pricing which can be very subjective depending on the context—what is 1200$ for dev tooling when you pay them more than $150k per year, yes it's US-centric but relevant. Python and SQL together in the dbt framework is your codebase. We will also cover what it means to manage dbt 1.

Management 130

Management Pipeline-centric Database-centric SQL 130

Christophe Blefari

APRIL 8, 2023

The only normalisation I did was back at the engineering school while learning SQL with Normal Forms. Actually what I cared was physical storage, data formats, logical partitioning or indexing. I still firmly believe that this is not the role of a data engineer. Enters the analytics engineer.

Pipeline-centric 130

Pipeline-centric Database-centric Algorithm Data 130

Christophe Blefari

APRIL 8, 2023

The only normalisation I did was back at the engineering school while learning SQL with Normal Forms. Actually what I cared was physical storage, data formats, logical partitioning or indexing. I still firmly believe that this is not the role of a data engineer. Enters the analytics engineer.

Pipeline-centric 130

Pipeline-centric Database-centric Algorithm Data 130

DataKitchen

FEBRUARY 21, 2023

On 24 January 2023, Gartner released the article “ 5 Ways to Enhance Your Data Engineering Practices.” Data team morale is consistent with DataKitchen’s own research. We surveyed 600 data engineers , including 100 managers, to understand how they are faring and feeling about the work that they are doing.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Monte Carlo

MARCH 28, 2024

As well as answering that question, we’ll be delving into how they differ from data scientists and data engineers. We’ll also be covering what they do day to day, what sort of salaries they command, and how individuals might go about pivoting to become a DataOps engineer. What does a DataOps engineer do?

Pipeline-centric 75

Pipeline-centric Engineering BI Google Cloud 75

Data Engineering Podcast

AUGUST 13, 2022

Summary Data engineers have typically left the process of data labeling to data scientists or other roles because of its nature as a manual and process heavy undertaking, focusing instead on building automation and repeatable systems. Data stacks are becoming more and more complex.

Machine Learning 100

Machine Learning Pipeline-centric Database-centric MongoDB 100

Meltano

JANUARY 25, 2023

A curated list of the top 9 must read blogs on data. The data world is in turmoil and lots of exciting things happen every day, week and year. At Meltano we’re ourselves avid users of data, data engineers, data PMs, and data enthusiasts through and through. Happy reading!

Pipeline-centric 52

Pipeline-centric Database-centric Data Warehouse SQL 52

Snowflake

JUNE 28, 2023

Since its launch two years ago, Snowpark has been empowering data scientists, data engineers, and application developers to streamline their architectures, accelerate development, and boost the performance of data engineering and ML/AI workloads on Snowflake.

Python 52

Python Accessible Accessibility Pipeline-centric 52

Snowflake

JUNE 28, 2023

At Snowflake, we’re helping data scientists, data engineers, and application developers build faster and more efficiently in the Data Cloud. 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

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

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.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

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

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

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

DataKitchen

NOVEMBER 4, 2021

It often takes months to progress from a data lake to the final delivery of insights. One data engineer called it the “last mile problem.” . In our many conversations about data analytics, data engineers, analysts and scientists have verbalized the difficulty of creating analytics in the modern enterprise.

Process 98

Process Data Process Pharmaceutical Data Lake 98

Databand.ai

DECEMBER 20, 2022

He is also an open-source developer at The Apache Software Foundation and the author of Hysterical , a popular blog on tech careers and topics like data, coding, and engineering. On LinkedIn, he posts frequently about data engineering, data architecture, interview preparation, and career advice.

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 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

Confluent

JUNE 18, 2019

Who has never seen an application use RDBMS SQL statements to run searches? Using SQL to run your search might be enough for your use case, but as your project requirements grow and more advanced features are needed—for example, enabling synonyms, multilingual search, or even machine learning—your relational database might not be enough.

Architecture 94

Architecture Building Kafka Database-centric 94

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. Can I see the pipeline? Can I see the data source?’”

Pipeline-centric 52

Pipeline-centric Database-centric Metadata Unstructured Data 52

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.

Healthcare 52

Healthcare Data Pipeline Medical Pipeline-centric 52

Monte Carlo

JANUARY 9, 2024

We first heard these terrifying anecdotes when we gathered a panel of data experts: Gable CEO and co-founder Chad Sanderson , Ternary Data CEO and author of O’Reilly Data Engineering Fundamentals Joe Reis , and Capital One Senior Manager of Analytics & Data Science Emily Riederer.

Pipeline-centric 52

Pipeline-centric Database-centric Data Manufacturing 52

AltexSoft

JUNE 7, 2021

MapReduce performs batch processing only and doesn’t fit time-sensitive data or real-time analytics jobs. Data engineers who previously worked only with relational database management systems and SQL queries need training to take advantage of Hadoop. Cassandra excels at streaming data analysis. Data access options.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

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

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 29, 2022

She regularly contributes to IBM’s Journey to AI blog and shares her advice on LinkedIn around data strategy, data science, women in AI, data and analytics, data governance, and artificial intelligence. On LinkedIn, he posts regularly about AI, data, data science, data engineering, and machine learning.

BI 52

BI Consulting Data Science Data Governance 52

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. On the other hand, Data Scientists work in coordination with data analysts and data engineers.

Machine Learning 52

Machine Learning Engineering Pipeline-centric Database-centric 52