AWS Machine Learning Blog

APRIL 3, 2025

Amazon AppFlow was used to facilitate the smooth and secure transfer of data from various sources into ODAP. Additionally, Amazon Simple Storage Service (Amazon S3) served as the central data lake, providing a scalable and cost-effective storage solution for the diverse data types collected from different systems.

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Flipboard

DECEMBER 6, 2023

In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution. RAG with semantic search – Conventional RAG with semantic search was the last step before moving to SQL generation.

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ODSC - Open Data Science

SEPTEMBER 12, 2023

Although setting up a database to run your analyses may seem like an arduous task, modern open-source time series databases can provide significant benefits to any scientist running time series analysis on a large data set — and with much less effort than you might imagine. Interested in attending an ODSC event?

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Recent events including Tropical Cyclone Gabrielle have highlighted the susceptibility of the grid to extreme weather and emphasized the need for climate adaptation with resilient infrastructure. The model is then trained using a fully managed infrastructure, validated, and published to the Amazon SageMaker Model Registry.

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phData

JUNE 11, 2025

Depending on the requirement, it is important to choose between transient and permanent tables, as well as data recovery needs and downtime considerations. We can set the STATEMENT_TIMEOUT_IN_SECONDS parameter to define the maximum time a SQL statement can run before it is canceled.

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Clustering SQL Database Data Lakes 59

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.

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ODSC - Open Data Science

APRIL 25, 2025

One of the main drivers for this problem is that most analytic systems are built within the context of a unified SQL environment. Getting all the data together, in one place, and integrated is generally the main goal of ourwork. Unfortunately in analytic systems, we have been doing little other than tight coupling for decades.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

Configure the following scopes on your connected app: Manage user data via APIs ( api ). Perform ANSI SQL queries on Salesforce Data Cloud data (Data Cloud_query_api ). Manage Data Cloud profile data ( Data Cloud_profile_api ). Drag and drop the file, then choose Edit in SQL.

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AWS Machine Learning Blog

APRIL 18, 2023

With the recently launched Amazon Monitron Kinesis data export v2 feature , your OT team can stream incoming measurement data and inference results from Amazon Monitron via Amazon Kinesis to AWS Simple Storage Service (Amazon S3) to build an Internet of Things (IoT) data lake. The latest version of Firefox or Chrome.

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phData

APRIL 29, 2024

Recognizing these specific needs, Fivetran has developed a range of connectors, including dedicated applications, databases, files, and events, which can accommodate the diverse formats used by healthcare systems. Addressing these needs may pose challenges that lead to the implementation of custom solutions rather than a uniform approach.

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SQL Data Warehouse Azure Cloud Data 52

Pickl AI

FEBRUARY 23, 2025

Apache Spark Apache Spark is a unified analytics engine for Big Data processing, with built-in modules for streaming, SQL, Machine Learning , and graph processing. Key Features : Speed : Spark processes data in-memory, making it up to 100 times faster than Hadoop MapReduce in certain applications.

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ODSC - Open Data Science

JULY 22, 2023

We had bigger sessions on getting started with machine learning or SQL, up to advanced topics in NLP, and how to make deepfakes. Expo Hall ODSC events are more than just data science training and networking events. Top Sessions With sessions both online and in-person in Boston, there was something for everyone.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

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DataRobot Blog

MARCH 7, 2023

The DataRobot AI Platform seamlessly integrates with Azure cloud services, including Azure Machine Learning, Azure Data Lake Storage Gen 2 (ADLS), Azure Synapse Analytics, and Azure SQL database. DATAROBOT LAUNCH EVENT From Vision to Value. For more information, visit [link].

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AWS Machine Learning Blog

MARCH 8, 2023

Data collection and ingestion The data collection and ingestion layer connects to all upstream data sources and loads the data into the data lake. Amazon Athena to provide developers and business analysts SQL access to the generated data for analysis and troubleshooting.

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Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

NOVEMBER 6, 2023

Example: models: my_project: events: # materialize all models in models/events as tables +materialized: table csvs: # this is redundant, and does not need to be set +materialized: view We can also configure the materialization type inside the dbt SQL file or the yaml file. This is done via a create view statement.

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Clustering SQL Python Database 40

phData

FEBRUARY 5, 2024

The tool converts the templated configuration into a set of SQL commands that are executed against the target Snowflake environment. Replicate can interact with a wide variety of databases, data warehouses, and data lakes (on-premise or based in the cloud). It is also a helpful tool for learning a new SQL dialect.

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AWS Machine Learning Blog

MAY 31, 2024

Select the uploaded file and from Actions dropdown and choose the Query with S3 Select option to query the.csv data using SQL if the data was loaded correctly. In this demonstration, let’s assume that you need to remove the data related to a particular customer.

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The MLOps Blog

JUNE 27, 2023

LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities. It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. Notebook for interactive Python, SQL, and R editors for coding data pipelines.

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ODSC - Open Data Science

JANUARY 18, 2024

They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable. These professionals will work with their colleagues to ensure that data is accessible, with proper access. The reason this is an important skill is that ETL is a critical process for data warehousing and business intelligence.

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Data Engineering Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

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Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Data warehousing is a vital constituent of any business intelligence operation.

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DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. In contrast, such traditional query languages struggle to interpret unstructured data. This text has a lot of information, but it is not structured.

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Pickl AI

AUGUST 9, 2024

NoSQL Databases These databases, such as MongoDB, Cassandra, and HBase, are designed to handle unstructured and semi-structured data, providing flexibility and scalability for modern applications. Understanding the differences between SQL and NoSQL databases is crucial for students.

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phData

AUGUST 5, 2024

For instance, differential privacy adds noise to query results as a means of preventing access to Personally Identifiable Information (PII) and running multi-party computations directly on encrypted data. Object Tagging Tags are schema-level objects that allow data stewards to monitor sensitive data for compliance, protection, or discovery.

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Data Governance Data Quality Data Lakes ML 52

phData

JULY 10, 2023

Why External Tables are Important Data Ingestion: External tables allow you to easily load data into Snowflake from various external data sources without the need to first stage the data within Snowflake. Data Integration: Snowflake supports seamless integration with other data processing systems and data lakes.

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Data Lakes Azure Database AWS 40

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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Azure Data Scientist Data Science Machine Learning 52

phData

FEBRUARY 14, 2023

What Are the Best Third-Party Data Ingestion Tools for Snowflake? Fivetran Fivetran is a tool dedicated to replicating applications, databases, events, and files into a high-performance data warehouse, such as Snowflake. To help you make your choice, here are the ones we consider to be the best.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Alation

APRIL 14, 2022

Data pipeline orchestration. Support for languages and SQL. Moving/integrating data in the cloud/data exploration and quality assessment. Supports the ability to interact with the actual data and perform analysis on it. Pushing data to a data lake and assuming it is ready for use is shortsighted.

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Data Governance ML ML Cloud Data 59

The MLOps Blog

MARCH 21, 2023

The service will consume the features in real time, generate predictions in near real-time , such as in an event processing pipeline, and write the outputs to a prediction queue. I have worked with customers where R and SQL were the first-class languages of their data science community. Data engineers are mostly in charge of it.

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Machine Learning Machine Learning Data Scientist ML 69

The MLOps Blog

JUNE 5, 2023

One of the hardest things about MLOps today is that a lot of data scientists aren’t native software engineers, but it may be possible to lower the bar to software engineering. And so those are more sideshows of the conversations or other complementary pieces, maybe. Thank you for sharing that, David.

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AWS Machine Learning Blog

JANUARY 26, 2024

Plan for rollback and recovery from production security events and service disruptions such as prompt injection, training data poisoning, model denial of service, and model theft early on, and define the mitigations you will use as you define application requirements.

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phData

SEPTEMBER 27, 2024

Methods that allow our customer data models to be as dynamic and flexible as the customers they represent. In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more.

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