phData

APRIL 29, 2024

Oracle – The Oracle connector, a database-type connector, enables real-time data transfer of large volumes of data from on-premises or cloud sources to the destination of choice, such as a cloud data lake or data warehouse. and delivers them to analytics platforms downstream.

SQL 52

SQL Azure Data Warehouse Cloud Data 52

AWS Machine Learning Blog

JANUARY 26, 2024

We also discuss common security concerns that can undermine trust in AI, as identified by the Open Worldwide Application Security Project (OWASP) Top 10 for LLM Applications , and show ways you can use AWS to increase your security posture and confidence while innovating with generative AI.

AWS 122

AWS AI AI ML 122

phData

MARCH 22, 2024

Snowflake-managed Iceberg table’s performance is at par with Snowflake native tables while storing the data in public cloud storage. They are Ideal for situations where the data is already stored in data lakes and do not intend to load into Snowflake but need to use the features and performance of Snowflake.

SQL 52

SQL AWS Database Data Lakes 52

AWS Machine Learning Blog

JUNE 13, 2023

The natural language capabilities allow non-technical users to query data through conversational English rather than complex SQL. The AI and language models must identify the appropriate data sources, generate effective SQL queries, and produce coherent responses with embedded results at scale.

Database 72

Database SQL AWS AI 72

AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL.

SQL 111

SQL AWS Database ML 111

Data Science 101

NOVEMBER 7, 2019

Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Synapse allows one to use SQL to query petabytes of data, both relational and non-relational, with amazing speed. Azure Synapse. I think this announcement will have a very large and immediate impact.

Data Science 106

Data Science Azure SQL Machine Learning 106

ODSC - Open Data Science

APRIL 3, 2023

Data analysts often must go out and find their data, process it, clean it, and get it ready for analysis. This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing. Cloud Services: Google Cloud Platform, AWS, Azure.

Analytics 52

Analytics Analytics Data Analyst SQL 52

ODSC - Open Data Science

FEBRUARY 2, 2023

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. Big Data As datasets become larger and more complex, knowing how to work with them will be key.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

The MLOps Blog

JANUARY 23, 2023

These tools may have their own versioning system, which can be difficult to integrate with a broader data version control system. For instance, our data lake could contain a variety of relational and non-relational databases, files in different formats, and data stored using different cloud providers. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

Pickl AI

APRIL 6, 2023

Consequently, here is an overview of the essential requirements that you need to have to get a job as an Azure Data Engineer. In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. Data Warehousing concepts and knowledge should be strong. What is Polybase?

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

AWS Machine Learning Blog

AUGUST 4, 2023

The following steps give an overview of how to use the new capabilities launched in SageMaker for Salesforce to enable the overall integration: Set up the Amazon SageMaker Studio domain and OAuth between Salesforce and the AWS account s. The endpoint will be exposed to Salesforce Data Cloud as an API through API Gateway.

ML 78

ML ML AWS AI 78

phData

FEBRUARY 14, 2023

Qlik Replicate Qlik Replicate is a data integration tool that supports a wide range of source and target endpoints with configuration and automation capabilities that can give your organization easy, high-performance access to the latest and most accurate data. Matillion is not a no-code solution, but rather a low-code solution.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 83

AWS Data Lakes Clustering Data Preparation 83

IBM Journey to AI blog

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. Let’s delve into the database portfolio from IBM available on AWS. 

AWS 83

AWS Database AI AI 83

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.

Data Lakes 40

Data Lakes Azure Database AWS 40

AWS Machine Learning Blog

JUNE 5, 2023

To simplify access to Parquet files, Amazon SageMaker Canvas has added data import capabilities from over 40 data sources , including Amazon Athena , which supports Apache Parquet. Canvas provides connectors to AWS data sources such as Amazon Simple Storage Service (Amazon S3), Athena, and Amazon Redshift.

Machine Learning 73

Machine Learning Machine Learning AWS Data Lakes 73

AWS Machine Learning Blog

MARCH 8, 2023

This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. In this post, you will learn how Marubeni is optimizing market decisions by using the broad set of AWS analytics and ML services, to build a robust and cost-effective Power Bid Optimization solution.

AWS 73

AWS Machine Learning Machine Learning Analytics 73

phData

AUGUST 9, 2023

ETL systems just couldn’t handle the massive flows of raw data. Open source big data tools like Hadoop were experimented with – these could land data into a repository first before transformation. Thus, the early data lakes began following more of the EL-style flow.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52

ODSC - Open Data Science

JANUARY 18, 2024

So as you take inventory of your existing skill set, you’ll want to start to identify the areas where you need to focus on to become a data engineer. These areas may include SQL, database design, data warehousing, distributed systems, cloud platforms (AWS, Azure, GCP), and data pipelines.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

JANUARY 4, 2024

One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing with complex technical code, business users and data analysts can ask questions related to data and insights in plain language.

SQL 119

SQL Database AI AI 119

Alation

FEBRUARY 20, 2020

For many enterprises, a hybrid cloud data lake is no longer a trend, but becoming reality. Due to these needs, hybrid cloud data lakes emerged as a logical middle ground between the two consumption models. AWS Propelling Hybrid Cloud Environments. The Problem with Hybrid Cloud Environments.

Data Lakes 52

Data Lakes Cloud Data AWS Data Governance 52

Flipboard

DECEMBER 6, 2023

needed to address some of these challenges in one of their many AI use cases built on AWS. 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.

SQL 130

SQL Database AWS Machine Learning 130

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. If you want to do the process in a low-code/no-code way, you can follow option C.

ML 93

ML ML AWS Data Warehouse 93

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. Uses secure protocols for data security.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

ODSC - Open Data Science

JULY 27, 2023

Choosing a Data Lake Format: What to Actually Look For The differences between many data lake products today might not matter as much as you think. When choosing a data lake, here’s something else to consider. When choosing a data lake, here’s something else to consider.

Data Lakes 52

Data Lakes AI AI SQL 52

IBM Journey to AI blog

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data fabric: A mostly new architecture.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

AWS Machine Learning Blog

APRIL 18, 2023

It includes sensor devices to capture vibration and temperature data, a gateway device to securely transfer data to the AWS Cloud, the Amazon Monitron service that analyzes the data for anomalies with ML, and a companion mobile app to track potential failures in your machinery.

AWS 66

AWS ML ML Database 66

Alation

NOVEMBER 18, 2021

External Tables Create a Shared View of the Data Lake. We’ve seen external tables become popular with our customers, who use them to provide a normalized relational schema on top of their data lake. Essentially, external tables create a shared view of the data lake, a single pane of glass everyone can reference.

Data Lakes 52

Data Lakes Data Governance SQL AWS 52

ODSC - Open Data Science

APRIL 24, 2023

To cluster the data we have to calculate distances between IPs — The number of all possible IP pairs is very large, and we had to solve the scale problem. Data Processing and Clustering Our data is stored in a Data Lake and we used PrestoDB as a query engine. Ori also has an AWS Data Analytics certification.

Clustering 52

Clustering SQL Algorithm ML 52

The MLOps Blog

OCTOBER 20, 2023

Example template for an exploratory notebook | Source: Author How to organize code in Jupyter notebook For exploratory tasks, the code to produce SQL queries, pandas data wrangling, or create plots is not important for readers. in a pandas DataFrame) but in the company’s data warehouse (e.g., documentation.

SQL 52

SQL Database Data Scientist Python 52

IBM Journey to AI blog

JULY 17, 2023

Watsonx.data is built on 3 core integrated components: multiple query engines, a catalog that keeps track of metadata, and storage and relational data sources which the query engines directly access. Integrations between watsonx.data and AWS solutions include Amazon S3, EMR Spark, and later this year AWS Glue, as well as many more to come.

AI 67

AI AI Machine Learning Machine Learning 67

Flipboard

DECEMBER 18, 2023

Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more.

AWS 112

AWS Data Warehouse ETL SQL 112

phData

DECEMBER 4, 2023

If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Copy Into When loading data into Snowflake, the very first and most important rule to follow is: do not load data with SQL inserts!

Clustering 98

Clustering Database SQL Data Pipeline 98

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Alation

JULY 18, 2022

And the highlight, for us data intelligence folks, was the Databricks’ announcement that Unity Catalog , its unified governance solution for all data assets on its Lakehouse platform, will soon be available on AWS and Azure in the upcoming weeks. A simple model to control access to data via a UI or SQL.

AI 52

AI AI Data Lakes Azure 52

The MLOps Blog

MARCH 21, 2023

I have worked with customers where R and SQL were the first-class languages of their data science community. If your organization runs its workloads on AWS , it might be worth it to leverage AWS SageMaker. Solution Data lakes and warehouses are the two key components of any data pipeline.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

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.

ML 73

ML ML AWS SQL 73