Analytics Vidhya

FEBRUARY 13, 2023

Introduction Every data scientist demands an efficient and reliable tool to process this big unstoppable data. Today we discuss one such tool called Delta Lake, which data enthusiasts use to make their data processing pipelines more efficient and reliable.

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Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. What is data engineering?

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Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

AUGUST 1, 2023

Aspiring and experienced Data Engineers alike can benefit from a curated list of books covering essential concepts and practical techniques. These 10 Best Data Engineering Books for beginners encompass a range of topics, from foundational principles to advanced data processing methods. What is Data Engineering?

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Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

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ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

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ODSC - Open Data Science

JANUARY 30, 2024

Data engineering is a hot topic in the AI industry right now. And as data’s complexity and volume grow, its importance across industries will only become more noticeable. But what exactly do data engineers do? So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest.

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Data Science Blog

MARCH 3, 2023

der Aufbau einer Datenplattform, vielleicht ein Data Warehouse zur Datenkonsolidierung, Process Mining zur Prozessanalyse oder Predictive Analytics für den Aufbau eines bestimmten Vorhersagesystems, KI zur Anomalieerkennung oder je nach Ziel etwas ganz anderes. Es gibt aber viele junge Leute, die da gerne einsteigen wollen.

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Pickl AI

SEPTEMBER 5, 2024

Summary: This blog provides a comprehensive roadmap for aspiring Azure Data Scientists, outlining the essential skills, certifications, and steps to build a successful career in Data Science using Microsoft Azure. This roadmap aims to guide aspiring Azure Data Scientists through the essential steps to build a successful career.

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Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

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Data Engineering Data Engineering Data Engineer Data Engineering 52

Flipboard

DECEMBER 11, 2024

Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data.

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phData

NOVEMBER 8, 2024

Data Versioning and Time Travel Open Table Formats empower users with time travel capabilities, allowing them to access previous dataset versions. Each snapshot has a separate manifest file that keeps track of the data files associated with that snapshot and hence can be restored/queries whenever needed. FAQs What is a Data Lakehouse?

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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AWS Machine Learning Blog

DECEMBER 29, 2022

Many of the RStudio on SageMaker users are also users of Amazon Redshift , a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

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Dataconomy

APRIL 27, 2023

Conversely, OLAP systems are optimized for conducting complex data analysis and are designed for use by data scientists, business analysts, and knowledge workers. OLAP systems support business intelligence, data mining, and other decision support applications. An OLAP database may also be organized as a data warehouse.

Database 159

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Smart Data Collective

OCTOBER 17, 2022

Engineering teams, in particular, can quickly get overwhelmed by the abundance of information pertaining to competition data, new product and service releases, market developments, and industry trends, resulting in information anxiety. Explosive data growth can be too much to handle. Can’t get to the data.

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AWS Machine Learning Blog

MARCH 11, 2024

The new VistaPrint personalized product recommendation system Figure 1 As seen in Figure 1, the steps in how VistaPrint provides personalized product recommendations with their new cloud-native architecture are: Aggregate historical data in a data warehouse. Transform the data to create Amazon Personalize training data.

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Alation

JULY 19, 2022

These days, data scientists are in high demand. Across the country, data scientists have an unemployment rate of 2% and command an average salary of nearly $100,000. For these reasons, finding and evaluating data is often time-consuming. How Data Catalogs Help Data Scientists Ask Better Questions.

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Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

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Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

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AI AI Tableau Data Scientist 95

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

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ODSC - Open Data Science

OCTOBER 7, 2024

Google Cloud: Empowers data scientists with scalable AI infrastructure, from training large models to real-time inference using NVIDIA GPUs​ Even before the conference starts, you connect with AI and Google Cloud experts at the upcoming virtual webinar on October 9th with Machine Learning and AI expert Lavi Nigam.

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Data Scientist Data Science Machine Learning Machine Learning 52

Alation

SEPTEMBER 7, 2021

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. But governance is a time-consuming process (for users and data stewards alike).

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Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

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IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities.

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AI AI Machine Learning Machine Learning 74

The MLOps Blog

MAY 17, 2023

This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines. This allows data scientists to keep their focus on the creation of models or their continuous improvement.

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ODSC - Open Data Science

JUNE 7, 2023

Data has to be stored somewhere. Data warehouses are repositories for your cleaned, processed data, but what about all that unstructured data your organization is starting to notice? What is a data lake? Snowflake Snowflake is a cross-cloud platform that looks to break down data silos.

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phData

SEPTEMBER 19, 2023

A rigid data model such as Kimball or Data Vault would ruin this flexibility and essentially transform your data lake into a data warehouse. However, some flexible data modeling techniques can be used to allow for some organization while maintaining the ease of new data additions.

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phData

MARCH 1, 2024

This process introduces considerable time and effort into the overall data ingestion workflow, delaying the availability of data to end consumers. Fortunately, the client has opted for Snowflake Data Cloud as their target data warehouse. This is incredibly useful for both Data Engineers and Data Scientists.

Data Engineering 52

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IBM Journey to AI blog

JULY 6, 2023

Challenges of data science Across most companies, finding, cleaning and preparing the proper data for analysis can take up to 80% of a data scientist’s day. Data from various sources, collected in different forms, require data entry and compilation.

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ODSC - Open Data Science

APRIL 4, 2024

There are many factors, but here, we’d like to hone in on the activities that a data science team engages in. More Speakers and Sessions Announced for the 2024 Data Engineering Summit Ranging from experimentation platforms to enhanced ETL models and more, here are some more sessions coming to the 2024 Data Engineering Summit.

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Alation

OCTOBER 18, 2022

Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Making this data visible in the data catalog will let data teams share their work, support re-use, and empower everyone to better understand and trust data.

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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And one of the biggest challenges that we see is taking an idea, an experiment, or an ML experiment that data scientists might be running in their notebooks and putting that into production.

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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And one of the biggest challenges that we see is taking an idea, an experiment, or an ML experiment that data scientists might be running in their notebooks and putting that into production.

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IBM Journey to AI blog

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Data Quality 52

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Alation

MAY 24, 2022

By augmenting rich human data curation in Alation with purpose-built data quality from partners, customers will have a complete view into the trustworthiness of data. It is advantageous to data consumers, such as data analysts and data scientists , to connect data quality context into their workflow in Alation.

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