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. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

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ODSC - Open Data Science

DECEMBER 19, 2023

Data Engineer Data engineers are responsible for the end-to-end process of collecting, storing, and processing data. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines. Interested in attending an ODSC event?

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Alation

FEBRUARY 13, 2020

Andreas Kohlmaier, Head of Data Engineering at Munich Re 1. --> Ron Powell, independent analyst and industry expert for the BeyeNETWORK and executive producer of The World Transformed FastForward Series, interviews Andreas Kohlmaier, Head of Data Engineering at Munich Re.

Data Lakes 52

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Despite the benefits of this architecture, Rocket faced challenges that limited its effectiveness: Accessibility limitations: The data lake was stored in HDFS and only accessible from the Hadoop environment, hindering integration with other data sources. This also led to a backlog of data that needed to be ingested.

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Data Science AWS Hadoop Data Scientist 78

ODSC - Open Data Science

MAY 7, 2024

Beyond his technical achievements, James is a sought-after speaker and is a prolific voice in the data community through his blog, JamesSerra.com. James Serra discusses data lakehouses, which merge data lakes and data warehouses. It lets you store a wide variety of data in a cost-effective way, like a data lake.

Data Warehouse 52

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AWS Machine Learning Blog

JUNE 20, 2024

Imperva Cloud WAF protects hundreds of thousands of websites against cyber threats and blocks billions of security events every day. Counters and insights based on security events are calculated daily and used by users from multiple departments. The data is stored in a data lake and retrieved by SQL using Amazon Athena.

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AWS Machine Learning Blog

DECEMBER 18, 2023

The triggers need to be scheduled to write the data to S3 at a period frequency based on the business need for training the models. Prior joining AWS, as a Data/Solution Architect he implemented many projects in Big Data domain, including several data lakes in Hadoop ecosystem.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Collaboration across teams – Shared features allow disparate teams like fraud, marketing, and sales to collaborate on building ML models using the same reliable data instead of creating siloed features. Audit trail for compliance – Administrators can monitor feature usage by all accounts centrally using CloudTrail event logs.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Data scientists will typically perform data analytics when collecting, cleaning and evaluating data. By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred.

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Data Science Analytics Analytics Data Scientist 94

ODSC - Open Data Science

OCTOBER 20, 2023

HPCC Systems — The Kit and Kaboodle for Big Data and Data Science Bob Foreman | Software Engineering Lead | LexisNexis/HPCC Join this session to learn how ECL can help you create powerful data queries through a comprehensive and dedicated data lake platform. Interested in attending an ODSC event?

AI 52

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AWS Machine Learning Blog

FEBRUARY 7, 2025

Data Governance Account This account hosts data governance services for data lake, central feature store, and fine-grained data access. The SageMaker Project Portfolio has SageMaker projects that data scientists and ML engineers can use to accelerate model training and deployment.

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ODSC - Open Data Science

MARCH 11, 2024

Data Morph: A Cautionary Tale of Summary Statistics Visualization in Bayesian Workflow Using Python or R Harnessing Bayesian Statistics for Business-Centric Data Science Data Engineering and Big Data Join this track to learn the latest techniques and processes to analyze raw data and automate data into mechanical processes and algorithms.

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IBM Journey to AI blog

OCTOBER 16, 2023

Curated foundation models, such as those created by IBM or Microsoft, help enterprises scale and accelerate the use and impact of the most advanced AI capabilities using trusted data. In addition to natural language, models are trained on various modalities, such as code, time-series, tabular, geospatial and IT events data.

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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.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

ODSC - Open Data Science

OCTOBER 3, 2024

Building an Effective OSS Management Layer for Your Data Lake Ahead of her ODSC West session on OSS management layers, the speaker discusses how data lakes can benefit from this system. Apply now for a chance to participate, and we’ll be in touch if space is available.

Data Science 40

Data Science Data Lakes Data Scientist AI 40

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

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Alation

MARCH 1, 2022

Through Impact Analysis, users can determine if a problem occurred with data upstream, and locate the impacted data downstream. With robust data lineage, data engineers can find and fix issues fast and prevent them from recurring. Similarly, analysts gain a clear view of how data is created.

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ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. You can also get data science training on-demand wherever you are with our Ai+ Training platform.

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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.

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Data Warehouse Azure AWS Database 59

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. You can use Snowflake cloud computing to store raw data in structured or variant format, using various data models to meet the needs.

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Data Warehouse Business Intelligence Business Intelligence Database 52

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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Pickl AI

JULY 25, 2024

Must Read Blogs: Exploring the Power of Data Warehouse Functionality. Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. Measures: Measures are the actual numerical values users analyse.

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Data Warehouse Business Intelligence Business Intelligence Database 52

ODSC - Open Data Science

FEBRUARY 15, 2024

The Future of the Single Source of Truth is an Open Data Lake Organizations that strive for high-performance data systems are increasingly turning towards the ELT (Extract, Load, Transform) model using an open data lake. See them here!

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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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The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate. Allegro.io

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Machine Learning Machine Learning Data Scientist ML 69

DataRobot Blog

OCTOBER 3, 2017

Enterprise data architects, data engineers, and business leaders from around the globe gathered in New York last week for the 3-day Strata Data Conference , which featured new technologies, innovations, and many collaborative ideas. 2) When data becomes information, many (incremental) use cases surface.

Data Lakes 52

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AWS Machine Learning Blog

NOVEMBER 1, 2024

At the same time, global health awareness and investments in clinical research have increased as a result of motivations by major events like the COVID-19 pandemic. The pipeline approach to data management in clinical trials has led to siloed, disconnected data across an organization, because separate storage is used for each trial.

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The MLOps Blog

JUNE 5, 2023

When I was at Ford, we needed to hook things up to the car and telemetry it out and download all that data somewhere and make a data lake and hire a team of people to sort that data and make it usable; the blocker of doing any ML was changing cars and building data lakes and things like that.

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