Smart Data Collective

NOVEMBER 8, 2020

This explains the current surge in demand for data engineers, especially in data-driven companies. That said, if you are determined to be a data engineer , getting to know about big data and careers in big data comes in handy. Similarly, various tools used in data engineering revolve around Scala.

Data Engineering 104

Data Engineering Data Engineering Data Engineering Data Engineer 104

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?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

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. These models may include regression, classification, clustering, and more.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Dataconomy

MARCH 19, 2025

Key disciplines involved in data science Understanding the core disciplines within data science provides a comprehensive perspective on the field’s multifaceted nature. Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91

ODSC - Open Data Science

APRIL 24, 2023

Botnet Detection at Scale — Lessons Learned From Clustering Billions of Web Attacks Into Botnets Editor’s note: Ori Nakar is a speaker for ODSC Europe this June. Be sure to check out his talk, “ Botnet detection at scale — Lesson learned from clustering billions of web attacks into botnets ,” there! AS ip_1, r.ip AND l.ip < r.ip

Clustering 52

Clustering SQL Algorithm Data Science 52

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 created a challenge for data scientists to become productive.

Data Science 72

Data Science AWS Hadoop Data Scientist 72

Dataconomy

JULY 24, 2023

” Data management and manipulation Data scientists often deal with vast amounts of data, so it’s crucial to understand databases, data architecture, and query languages like SQL. Skills in manipulating and managing data are also necessary to prepare the data for analysis.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

phData

SEPTEMBER 20, 2023

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python , Java, and Scala. As a declarative language, SQL is very powerful in allowing users from all backgrounds to ask questions about data. What is Snowflake’s Snowpark? Why Does Snowpark Matter?

SQL 98

SQL Python Data Lakes Machine Learning 98

Smart Data Collective

FEBRUARY 23, 2022

AWS Athena is a query service that allows users to analyze data in S3 using standard SQL syntax. Both combined, you use SQL to query what’s stored in S3. In the back-end, their machine-learning optimization tools monitor cluster performance and data usage to detect bottlenecks and query performances. Wrapping up.

Data Lakes 124

Data Lakes AWS SQL Big Data 124

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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AWS Machine Learning Machine Learning Natural Language Processing 131

Applied Data Science

AUGUST 2, 2021

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 most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data.

Data Science 130

Data Science Data Scientist ML ML 130

AWS Machine Learning Blog

AUGUST 8, 2023

You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. With interactive sessions, you can choose Apache Spark or Ray to easily process large datasets, without worrying about cluster management.

AWS 87

AWS Clustering Machine Learning Machine Learning 87

Smart Data Collective

OCTOBER 14, 2019

Businesses need software developers that can help ensure data is collected and efficiently stored. They’re looking to hire experienced data analysts, data scientists and data engineers. With big data careers in high demand, the required skillsets will include: Apache Hadoop. NoSQL and SQL.

Big Data 99

Big Data Big Data Apache Hadoop Hadoop 99

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

IBM Data Science in Practice

JANUARY 13, 2025

It is a cloud-native approach, and it suits a small team that does not want to host, maintain, and operate a Kubernetes cluster alonewith all the resulting responsibilities (and costs). The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines.

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ETL Data Pipeline Database Data Warehouse 100

AWS Machine Learning Blog

MARCH 10, 2023

Aggregating and preparing large amounts of data is a critical part of ML workflow. Data scientists and data engineers use Apache Spark, Apache Hive, and Presto running on Amazon EMR for large-scale data processing. The following diagram represents the different components used in this solution. This is TLS enabled.

Clustering 97

Clustering AWS ML ML 97

ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. TensorFlow is desired for its flexibility for ML and neural networks, PyTorch for its ease of use and innate design for NLP, and scikit-learn for classification and clustering.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

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. The first insert statement loads data having c_custkey between 30001 and 40000 – INSERT INTO ib_customers2 SELECT *, '11111111111111' AS HASHKEY FROM snowflake_sample_data.tpch_sf1.customer

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

phData

APRIL 26, 2024

Overview By harnessing the power of the Snowflake-Spark connector, you’ll learn how to transfer your data efficiently while ensuring compatibility and reliability. Whether you’re a data engineer, analyst, or hobbyist, this blog will equip you with the knowledge and tools to confidently make this migration.

Hadoop 52

Hadoop Clustering AWS Database 52

phData

DECEMBER 4, 2023

Thirty seconds is a good default for human users; if you find that queries are regularly queueing, consider making your warehouse a multi-cluster that scales on-demand. Cluster Count If your warehouse has to serve many concurrent requests, you may need to increase the cluster count to meet demand.

Clustering 98

Clustering Database SQL Data Pipeline 98

Tableau

DECEMBER 1, 2021

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. Another key data computation moment was Hyper in v10.5 (Jan

Tableau 145

Tableau ML ML Database 145

ODSC - Open Data Science

AUGUST 15, 2023

Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Free and paid passes are available now–register here.

Machine Learning 52

Machine Learning Machine Learning Data Science Data Scientist 52

IBM Journey to AI blog

APRIL 29, 2024

Krishna Maheshwari from NeuroBlade highlighted their collaboration with the Velox community, introducing NeuroBlade’s SPU (SQL Processing Unit) and its transformative impact on Velox’s computational speed and efficiency. He shared insights into Velox Wave and Accelerators, showcasing its potential for acceleration.

Clustering 78

Clustering SQL Data Engineer Data Engineering 78

AWS Machine Learning Blog

OCTOBER 9, 2024

To start using CloudWatch anomaly detection, you first must ingest data into CloudWatch and then enable anomaly detection on the log group. Using Amazon Redshift ML for anomaly detection Amazon Redshift ML makes it easy to create, train, and apply machine learning models using familiar SQL commands in Amazon Redshift data warehouses.

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AWS ML ML Data Quality 90

Dataconomy

APRIL 27, 2023

They are also designed to handle concurrent access by multiple users and applications, while ensuring data integrity and transactional consistency. Examples of OLTP databases include Oracle Database, Microsoft SQL Server, and MySQL. Final words Back to our original question: What is an online transaction processing database?

Database 159

Database Data Scientist Data Mining Data Mining 159

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.

Python 52

Python ML ML SQL 52

AWS Machine Learning Blog

SEPTEMBER 19, 2023

It lets engineers provide simple data transformation functions, then handles running them at scale on Spark and managing the underlying infrastructure. This enables data scientists and data engineers to focus on the feature engineering logic rather than implementation details. Group by model_year_status.

ML 120

ML ML AWS SQL 120

Tableau

DECEMBER 1, 2021

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. Another key data computation moment was Hyper in v10.5 (Jan

Tableau 98

Tableau ML ML Database 98

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. It promotes a disciplined approach to data modeling, making it easier to ensure data quality and consistency across the ML pipelines. Saurabh Gupta is a Principal Engineer at Zeta Global.

AWS 118

AWS Machine Learning Machine Learning ML 118

phData

AUGUST 10, 2023

That said, dbt provides the ability to generate data vault models and also allows you to write your data transformations using SQL and code-reusable macros powered by Jinja2 to run your data pipelines in a clean and efficient way. The most important reason for using DBT in Data Vault 2.0

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Mlearning.ai

FEBRUARY 16, 2023

With the help of Snowflake clusters, organizations can effectively deal with both rush times and slowdowns since they ensure scalability upon demand. Data warehousing is a vital constituent of any business intelligence operation. This is the way to reduce the work of scanning excessive numbers of data files in cloud storage.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Dataconomy

JULY 28, 2023

How to become a data scientist Data transformation also plays a crucial role in dealing with varying scales of features, enabling algorithms to treat each feature equally during analysis Noise reduction As part of data preprocessing, reducing noise is vital for enhancing data quality.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

JUNE 7, 2024

Integration: Airflow integrates seamlessly with other data engineering and Data Science tools like Apache Spark and Pandas. Comprehensive Data Management: Supports data movement, synchronisation, quality, and management. Scalability: Designed to handle large volumes of data efficiently.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

phData

DECEMBER 7, 2023

This is why it makes them appropriate for storing and retrieving non-traditional data sources like documents, images, and audio files. Querying Mechanism Relational databases depend on SQL (Structured Query Language) for querying. You might ask for data that meets certain criteria (ex. into vector embeddings. And why stop there?

Database 52

Database Natural Language Processing Clustering SQL 52

phData

MARCH 29, 2024

Founded in 2014 by three leading cloud engineers, phData focuses on solving real-world data engineering, operations, and advanced analytics problems with the best cloud platforms and products. Over the years, one of our primary focuses became Snowflake and migrating customers to this leading cloud data platform.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52