AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. This also led to a backlog of data that needed to be ingested.

Data Science 68

Data Science AWS Hadoop Data Scientist 68

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. option("multiLine", "true").option("header",

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SQL AWS Data Lakes AI 160

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

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ETL Data Warehouse Data Quality Data Lakes 52

Data Science Dojo

JULY 3, 2024

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Applied Data Science

AUGUST 2, 2021

Team Building the right data science team is complex. With a range of role types available, how do you find the perfect balance of Data Scientists , Data Engineers and Data Analysts to include in your team? The Data Engineer Not everyone working on a data science project is a data scientist.

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Data Science Data Scientist ML ML 130

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. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Smart Data Collective

MAY 20, 2019

These regulations have a monumental impact on data processing and handling , consumer profiling and data security. Data scientists and analysts who understand the ramifications can help organizations navigate the guidelines, and are skilled in both data privacy and security are in high demand.

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Analytics Analytics Data Analyst Machine Learning 111

The MLOps Blog

SEPTEMBER 7, 2023

Data Scientists and ML Engineers typically write lots and lots of code. From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc.

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Machine Learning Machine Learning ETL ML 52

AWS Machine Learning Blog

JANUARY 17, 2024

To obtain such insights, the incoming raw data goes through an extract, transform, and load (ETL) process to identify activities or engagements from the continuous stream of device location pings. Data scientists can accomplish this process by connecting through Amazon SageMaker notebooks.

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Clustering AWS ML ML 122

IBM Journey to AI blog

MARCH 14, 2024

Db2 Warehouse fully supports open formats such as Parquet, Avro, ORC and Iceberg table format to share data and extract new insights across teams without duplication or additional extract, transform, load (ETL). This allows you to scale all analytics and AI workloads across the enterprise with trusted data. 

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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. The good news is that there are many skills that data scientists already have that are transferable to data engineering.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JULY 6, 2023

Hive is a data warehousing infrastructure built on top of Hadoop. It has the following features: It facilitates querying, summarizing, and analyzing large datasets Hadoop also provides a SQL-like language called HiveQL Hive allows users to write queries to extract valuable insights from structured and semi-structured data stored in Hadoop.

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Hadoop SQL Big Data Big Data 52

Alation

MAY 24, 2022

Prime examples of this in the data catalog include: Trust Flags — Allow the data community to endorse, warn, and deprecate data to signal whether data can or can’t be used. Data Profiling — Statistics such as min, max, mean, and null can be applied to certain columns to understand its shape.

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Data Quality Data Governance ETL Data Observability 98

ODSC - Open Data Science

SEPTEMBER 27, 2023

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Schema Enforcement: Data warehouses use a “schema-on-write” approach. This ensures data consistency and integrity.

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Data Lakes Data Warehouse Database Big Data 98

The MLOps Blog

OCTOBER 20, 2023

Jupyter notebooks have been one of the most controversial tools in the data science community. Nevertheless, many data scientists will agree that they can be really valuable – if used well. Data on its own is not sufficient for a cohesive story. in a pandas DataFrame) but in the company’s data warehouse (e.g.,

SQL 52

SQL Database Data Scientist Python 52

ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Alation

JANUARY 17, 2023

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. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. A Note on the Shift from ETL to ELT. Data scientists.

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phData

MARCH 1, 2024

There’s no need for developers or analysts to manually adjust table schemas or modify ETL (Extract, Transform, Load) processes whenever the source data structure changes. Time Efficiency – The automated schema detection and evolution features contribute to faster data availability.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

APRIL 6, 2023

Data Engineering is one of the most productive job roles today because it imbibes both the skills required for software engineering and programming and advanced analytics needed by Data Scientists. How to Become an Azure Data Engineer? Having experience using at least one end-to-end Azure data lake project.

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Azure Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 15, 2023

Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible. Importance of Data Lakes Data Lakes play a pivotal role in modern data analytics, providing a platform for Data Scientists and analysts to extract valuable insights from diverse data sources.

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AWS Machine Learning Blog

AUGUST 21, 2023

We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction. To demonstrate fine-grained data access permissions, we consider the following two users: David, a data scientist on the marketing team.

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AWS Data Lakes Clustering Data Preparation 92

Dataconomy

JULY 28, 2023

Data preprocessing ensures the removal of incorrect, incomplete, and inaccurate data from datasets, leading to the creation of accurate and useful datasets for analysis ( Image Credit ) Data completeness One of the primary requirements for data preprocessing is ensuring that the dataset is complete, with minimal missing values.

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Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

NOVEMBER 4, 2024

They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Their work ensures that data flows seamlessly through the organisation, making it easier for Data Scientists and Analysts to access and analyse information.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Alation

APRIL 4, 2023

Integrating helpful metadata into user workflows gives all people, from data scientists to analysts , the context they need to use data more effectively. The Benefits and Challenges of the Modern Data Stack Why are such integrations needed? This empowers users to judge data’s quality and fitness for purpose quickly.

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DataOps Data Engineering Data Engineering Data Engineering 52

Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

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Data Analyst Data Analysis Data Analysis Machine Learning 52

phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. A DataFrame is like a query that must be evaluated to retrieve data. An action causes the DataFrame to be evaluated and sends the corresponding SQL statement to the server for execution.

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Python ML ML SQL 52

phData

SEPTEMBER 4, 2024

ThoughtSpot is a cloud-based AI-powered analytics platform that uses natural language processing (NLP) or natural language query (NLQ) to quickly query results and generate visualizations without the user needing to know any SQL or table relations. Suppose your business requires more robust capabilities across your technology stack.

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Analytics Analytics SQL ETL 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. A lot of you who are already in the data science field must be familiar with BigQuery and its advantages.

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SQL Database Apache Hadoop Data Science 52

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

Alation

OCTOBER 27, 2022

Like so many data teams out there, we need to maximize our throughput whilst ensuring quality. Jason: I’m curious to learn about your modern data stack. Adrian Lievano, Senior Data Scientist, Alation : Most of our data sources are connected and extracted using Fivetran , and then transported to raw storage in Snowflake.

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Data Analyst Data Scientist Analytics Analytics 52

The MLOps Blog

JANUARY 23, 2023

When it comes to data complexity, it is for sure that in machine learning, we are dealing with much more complex data. First of all, machine learning engineers and data scientists often use data from different data vendors. Some data sets are being corrected by data entry specialists and manual inspectors.

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ML ML Data Lakes Machine Learning 52

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g.,

Data Engineer 40

Data Engineer Data Engineering Data Engineering Data Engineering 40

Pickl AI

NOVEMBER 14, 2023

Here we will upskill you with the Pandas library which stands as a highly favored asset amongst data scientists, facilitating seamless data manipulation and analysis. Alongside Matplotlib, a key tool for data visualization, and NumPy, the foundational library for scientific computing upon which Pandas was constructed.

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Python SQL Database Data Analysis 52

The MLOps Blog

AUGUST 3, 2023

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter.

ML 52

ML ML Data Scientist Machine Learning 52

DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.

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Machine Learning Machine Learning ML ML 59

ODSC - Open Data Science

SEPTEMBER 29, 2023

It truly is an all-in-one data lake solution. HPCC Systems and Spark also differ in that they work with distinct parts of the big data pipeline. Spark is more focused on data science, ingestion, and ETL, while HPCC Systems focuses on ETL and data delivery and governance. Tell me more about ECL.

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Data Lakes Clustering Big Data Big Data 52

AWS Machine Learning Blog

JANUARY 6, 2023

The rules in this engine were predefined and written in SQL, which aside from posing a challenge to manage, also struggled to cope with the proliferation of data from TR’s various integrated data source. TR customer data is changing at a faster rate than the business rules can evolve to reflect changing customer needs.

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