Data Science Connect

JANUARY 27, 2023

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

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Data Engineering Data Engineering Data Engineering Data Engineer 130

AWS Machine Learning Blog

DECEMBER 20, 2023

Whether logs are coming from Amazon Web Services (AWS), other cloud providers, on-premises, or edge devices, customers need to centralize and standardize security data. After the security log data is stored in Amazon Security Lake, the question becomes how to analyze it. Subscribe an AWS Lambda function to the SQS queue.

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AWS ML ML Algorithm 135

O'Reilly Media

SEPTEMBER 15, 2021

Cloud certifications, specifically in AWS and Microsoft Azure, were most strongly associated with salary increases. As we’ll see later, cloud certifications (specifically in AWS and Microsoft Azure) were the most popular and appeared to have the largest effect on salaries. The top certification was for AWS (3.9%

AI 145

AI AI Azure AWS 145

Smart Data Collective

SEPTEMBER 21, 2021

A data fabric solution must be capable of optimizing code natively using preferred programming languages in the data pipeline to be easily integrated into cloud platforms such as Amazon Web Services, Azure, Google Cloud, etc. This will enable the users to seamlessly work with code while developing data pipelines.

Data Quality 131

Data Quality Data Pipeline Database Internet of Things 131

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. Data Engineering Platforms Spark is still the leader for data pipelines but other platforms are gaining ground. Google Cloud is starting to make a name for itself as well.

Data Science 111

Data Science Deep Learning Deep Learning Natural Language Processing 111

Women in Big Data

NOVEMBER 27, 2024

How to Choose a Data Warehouse for Your Big Data Choosing a data warehouse for big data storage necessitates a thorough assessment of your unique requirements. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Around this time, industry observers reported NVIDIA’s strategy pivoting from its traditional gaming and graphics focus to moving into scientific computing and data analytics.

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AWS ML ML Clustering 116

phData

MAY 25, 2023

One big issue that contributes to this resistance is that although Snowflake is a great cloud data warehousing platform, Microsoft has a data warehousing tool of its own called Synapse. In a perfect world, Microsoft would have clients push even more storage and compute to its Azure Synapse platform.

Power BI 84

Power BI Analytics Analytics Azure 84

Pickl AI

MAY 15, 2024

As a Data Analyst, you’ve honed your skills in data wrangling, analysis, and communication. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

Data Analyst 52

Data Analyst Data Scientist Data Science Machine Learning 52

Towards AI

APRIL 4, 2023

We sketch out ideas in notebooks, build data pipelines and training scripts, and integrate with a vibrant ecosystem of Python tools. Edge Impulse provides powerful automations and low-code capabilities to make it easier to build valuable datasets and develop advanced AI with streaming data.

ML 98

ML ML Python Machine Learning 98

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

ODSC - Open Data Science

FEBRUARY 2, 2023

Cloud Services The only two to make multiple lists were Amazon Web Services (AWS) and Microsoft Azure. Most major companies are using one of the two, so excelling in one or the other will help any aspiring data scientist. Saturn Cloud is picking up a lot of momentum lately too thanks to its scalability.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

phData

APRIL 29, 2024

The phData team achieved a major milestone by successfully setting up a secure end-to-end data pipeline for a substantial healthcare enterprise. Our team frequently configures Fivetran connectors to cloud object storage platforms such as Amazon S3, Azure Blob Storage, and Google Cloud Storage.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

phData

FEBRUARY 14, 2023

If using a network policy with Snowflake, be sure to add Fivetran’s IP address list , which will ensure Azure Data Factory (ADF) Azure Data Factory is a fully managed, serverless data integration service built by Microsoft. Source data formats can only be Parquer, JSON, or Delimited Text (CSV, TSV, etc.).

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Pickl AI

OCTOBER 15, 2024

It supports both batch and real-time data processing , making it highly versatile. Its ability to integrate with cloud platforms like AWS and Azure makes it an excellent choice for businesses moving to the cloud. Apache Nifi Apache Nifi is an open-source ETL tool that automates data flow between systems.

ETL 52

ETL Azure AWS Data Governance 52

DataRobot Blog

OCTOBER 3, 2017

Many announcements at Strata centered on product integrations, with vendors closing the loop and turning tools into solutions, most notably: A Paxata-HDInsight solution demo, where Paxata showcased the general availability of its Adaptive Information Platform for Microsoft Azure. 3) Data professionals come in all shapes and forms.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52

Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

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.

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Machine Learning Machine Learning ML ML 134

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Read Further: Azure Data Engineer Jobs.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

phData

FEBRUARY 21, 2025

The Cloud represents an iteration beyond the on-prem data warehouse, where computing resources are delivered over the Internet and are managed by a third-party provider. Examples include: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

Data Warehouse 52

Data Warehouse Cloud Data ETL Cloud Computing 52

Pickl AI

APRIL 21, 2025

Feature Big Data Data Science Primary Focus Handling the characteristics of data (Volume, Velocity, Variety, Veracity) Extracting knowledge and insights from data Nature The data itself and the infrastructure to manage it The process and methods for analysing data Core Goal To store, process, and manage massive datasets efficiently To understand, interpret, (..)

Big Data 52

Big Data Big Data Data Science Machine Learning 52

DrivenData Labs

MAY 21, 2024

Some projects manage this folder like the data folder and sync it to a canonical store (e.g., AWS S3) separately from source code. Data storage ¶ V1 was designed to encourage data scientists to (1) separate their data from their codebase and (2) store their data on the cloud.

Data Science 233

Data Science Python Data Scientist Data Warehouse 233

IBM Journey to AI blog

AUGUST 12, 2024

It includes a range of technologies—including machine learning frameworks, data pipelines, continuous integration / continuous deployment (CI/CD) systems, performance monitoring tools, version control systems and sometimes containerization tools (such as Kubernetes )—that optimize the ML lifecycle.

Big Data 106

Big Data Big Data ML ML 106

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Big Data Processing: Apache Hadoop, Apache Spark, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

APRIL 9, 2025

Integration : Can it connect with existing systems like AWS, Azure, or Google Cloud? Airflow is particularly useful for organizations that require flexibility and scalability in their data pipelines. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

ODSC - Open Data Science

FEBRUARY 6, 2024

Apache Kafka For data engineers dealing with real-time data, Apache Kafka is a game-changer. This open-source streaming platform enables the handling of high-throughput data feeds, ensuring that data pipelines are efficient, reliable, and capable of handling massive volumes of data in real-time.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

DECEMBER 4, 2023

The software you might use OAuth with includes: Tableau Power BI Sigma Computing If so, you will need an OAuth provider like Okta, Microsoft Azure AD, Ping Identity PingFederate, or a Custom OAuth 2.0 When to use SCIM vs phData's Provision Tool SCIM manages users and groups with Azure Active Directory or Okta. authorization server.

Database 98

Database Clustering SQL Data Pipeline 98

phData

AUGUST 4, 2023

The external stage area includes Microsoft Azure Blob storage, Amazon AWS S3, and Google Cloud Storage. Amazon S3 for AWS, Azure Blob Storage for Azure, or Google Cloud Storage for GCP) to store the actual data files in micro-partitions. They are flexible, secure, and provide exceptional performance.

Database 52

Database Azure SQL AWS 52

phData

SEPTEMBER 15, 2023

Salesforce Sync Out is a crucial tool that enables businesses to transfer data from their Salesforce platform to external systems like Snowflake, AWS S3, and Azure ADLS. The Salesforce Sync Out connector moves Salesforce data directly into Snowflake, simplifying the data pipeline and reducing latency.

Data Warehouse 52

Data Warehouse Tableau Data Silos Analytics 52

Mlearning.ai

JULY 11, 2023

This includes important stages such as feature engineering, model development, data pipeline construction, and data deployment. For example, when it comes to deploying projects on cloud platforms, different companies may utilize different providers like AWS, GCP, or Azure.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

phData

SEPTEMBER 2, 2024

Best practices are a pivotal part of any software development, and data engineering is no exception. This ensures the data pipelines we create are robust, durable, and secure, providing the desired data to the organization effectively and consistently.

ETL 52

ETL Data Warehouse SQL Database 52

Precisely

MARCH 14, 2023

First, private cloud infrastructure providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) began by offering more cost-effective and elastic resources for fast access to infrastructure. Instead of moving customer data to the processing engine, we move the processing engine to the data.

Data Quality 52

Data Quality Cloud Data Data Pipeline Data Observability 52

DataRobot

SEPTEMBER 14, 2021

DataRobot now delivers both visual and code-centric data preparation and data pipelines, along with automated machine learning that is composable, and can be driven by hosted notebooks or a graphical user experience. Modular and Extensible, Building on Existing Investments.

AI 52

AI AI Data Pipeline Data Preparation 52

phData

JULY 2, 2024

Developers can seamlessly build data pipelines, ML models, and data applications with User-Defined Functions and Stored Procedures. If your data pipeline requirements are quite straightforward—i.e., You have different developers working on building data pipelines/UDFs/stored procedures in the same environment.

Python 52

Python SQL Data Pipeline ML 52

phData

OCTOBER 11, 2024

To help, phData designed and implemented AI-powered data pipelines built on the Snowflake AI Data Cloud , Fivetran, and Azure to automate invoice processing. Implementation of metadata-driven data pipelines for governance and reporting. This is where AI truly shines.

AI 52

AI AI Data Pipeline ML 52

The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams.

ML 52

ML ML Data Lakes Machine Learning 52