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. The platform includes several features that make it easy to develop and test data pipelines.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Business Intelligence 52

Smart Data Collective

OCTOBER 27, 2021

Druid is a real-time analytics database from Apache. It is a high-performing database that is designed to build fast, modern data applications. Druid is specifically designed to support workflows that require fast ad-hoc analytics, concurrency, and instant data visibility are core necessities. Druid has no initial price.

Analytics 115

Analytics Analytics Data Warehouse Business Intelligence 115

Smart Data Collective

OCTOBER 17, 2022

Data pipelines automatically fetch information from various disparate sources for further consolidation and transformation into high-performing data storage. There are a number of challenges in data storage , which data pipelines can help address. Choosing the right data pipeline solution.

Data Pipeline 132

Data Pipeline Data Warehouse ETL Exploratory Data Analysis 132

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 Engineer Data Engineering Data Engineering 52

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.

Data Science 83

Data Science Analytics Analytics Data Scientist 83

Ocean Protocol

JANUARY 17, 2024

We wanted to professionalize and operationalize the data pipeline, for use by simulation, the bots, and the analytics app. We wanted to extend simulation, into a flow that supported experiments on realtime data and with the possibility of live trading. It’s centered around a data lake with tiers from raw → refined.

Data Pipeline 52

Data Pipeline AI AI Analytics 52

Alation

MARCH 22, 2022

They created each capability as modules, which can either be used independently or together to build automated data pipelines. Alation’s governance capabilities include automated classification, profiling, data quality, lineage, stewardship, and deep policy integration with leading cloud-native databases like Snowflake.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineer 52

Precisely

JANUARY 19, 2023

Powerful data integration capabilities bridge the gap between mainframe systems and cloud platforms, replicating changes on the mainframe to cloud data platforms and on-premise databases in real time. Containerization Docker containers are revolutionizing the way organizations host and deply applications.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

The MLOps Blog

MAY 17, 2023

We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one. What is an ETL data pipeline in ML? Xoriant It is common to use ETL data pipeline and data pipeline interchangeably.

ETL 59

ETL Data Pipeline ML ML 59

phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

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. Snowflake Database Pros Extensive Storage Opportunities Snowflake provides affordability, scalability, and a user-friendly interface.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

IBM Journey to AI blog

OCTOBER 16, 2023

This type of next-generation data store combines a data lake’s flexibility with a data warehouse’s performance and lets you scale AI workloads no matter where they reside. It allows for automation and integrations with existing databases and provides tools that permit a simplified setup and user experience.

AI 76

AI AI Data Warehouse ML 76

IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. After all, Alex may not be aware of all the data available to her.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

DagsHub

DECEMBER 11, 2023

It does not support the ‘dvc repro’ command to reproduce its data pipeline. DVC Released in 2017, Data Version Control ( DVC for short) is an open-source tool created by iterative. Adding new data to the storage requires pulling the existing data, then calculating the new hash before pushing back the whole data.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

ODSC - Open Data Science

JANUARY 30, 2024

Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more. Think of data engineers as the architects of the data ecosystem.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

FEBRUARY 5, 2024

Creating the databases, schemas, roles, and access grants that comprise a data system information architecture can be time-consuming and error-prone. Luckily phData has created a template-driven Provision Tool that automates onboarding users and projects to Snowflake, allowing your data teams to start producing real value immediately.

SQL 52

SQL Database Data Quality Data Warehouse 52

ODSC - Open Data Science

FEBRUARY 15, 2024

These systems represent data as knowledge graphs and implement graph traversal algorithms to help find content in massive datasets. These systems are not only useful for a wide range of industries, they are fun for data engineers to work on.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

phData

DECEMBER 4, 2023

More on this topic later; but for now, keep in mind that the simplest method is to create a naming convention for database objects that allows you to identify the owner and associated budget. The extended period will allow you to perform Time Travel activities, such as undropping tables or comparing new data against historical values.

Clustering 98

Clustering Database SQL Data Pipeline 98

The MLOps Blog

JUNE 27, 2023

It integrates with Git and provides a Git-like interface for data versioning, allowing you to track changes, manage branches, and collaborate with data teams effectively. Dolt Dolt is an open-source relational database system built on Git. It could help you detect and prevent data pipeline failures, data drift, and anomalies.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

FEBRUARY 24, 2023

Organizations can unite their siloed data and securely share governed data while executing diverse analytic workloads. Snowflake’s engine provides a solution for data warehousing, data lakes, data engineering, data science, data application development, and data sharing.

Analytics 52

Analytics Analytics Database Python 52

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

Flipboard

NOVEMBER 24, 2023

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases.

ML 95

ML ML AWS AI 95

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Data Engineering 91

Data Engineering Data Engineer Data Engineering Data Engineering 91

Alation

JUNE 21, 2022

Why start with a data source and build a visualization, if you can just find a visualization that already exists, complete with metadata about it? Data scientists went beyond database tables to data lakes and cloud data stores. Data scientists want to catalog not just information sources, but models.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Iguazio

JUNE 25, 2023

Data Ingestion and Processing - MLOps enables data pipeline management and data quality monitoring. This is done by automating the ingestion of data from various sources, such as databases, data lakes, APIs, or streaming platforms.

ML 52

ML ML Clustering Cross Validation 52

Alation

APRIL 14, 2022

Data pipeline orchestration. Moving/integrating data in the cloud/data exploration and quality assessment. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged. Cloud governance.

Data Governance 59

Data Governance ML ML Cloud Data 59

AWS Machine Learning Blog

DECEMBER 20, 2023

A novel approach to solve this complex security analytics scenario combines the ingestion and storage of security data using Amazon Security Lake and analyzing the security data with machine learning (ML) using Amazon SageMaker. When considering terabyte scale, you should consider AWS SDK for pandas support for Modin.

AWS 106

AWS ML ML Algorithm 106

IBM Journey to AI blog

JULY 17, 2023

Through workload optimization across multiple query engines and storage tiers, organizations can reduce data warehouse costs by up to 50 percent. 1 Watsonx.data offers built-in governance and automation to get to trusted insights within minutes, and integrations with existing databases and tools to simplify setup and user experience.

AI 66

AI AI Machine Learning Machine Learning 66

Mlearning.ai

JULY 10, 2023

Without partitioning, daily data activities will cost your company a fortune and a moment will come where the cost advantage of GCP BigQuery becomes questionable. In prior to creating your first Scheduled Query, I recommend that you confirm with your database administrator that you have the adequate IAM permissions to create one.

SQL 52

SQL Database Database Administration Data Lakes 52

Snorkel AI

MARCH 2, 2023

We have data pipelines and data preparation. So each of them may require some repositories from a data lake house/analytics hub kind of thing for sharing data, to a feature store, to a model hub, to the responsible AI (known sets of things that you need to guard against), to a model registry. Think biology.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Snorkel AI

MARCH 2, 2023

We have data pipelines and data preparation. So each of them may require some repositories from a data lake house/analytics hub kind of thing for sharing data, to a feature store, to a model hub, to the responsible AI (known sets of things that you need to guard against), to a model registry. Think biology.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. The difficult part is what comes before training a model and then after.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. The difficult part is what comes before training a model and then after.

SQL 52

SQL ML ML Python 52

phData

NOVEMBER 9, 2023

A cloud data warehouse is designed to combine a concept that every organization knows, namely a data warehouse, and optimizes the components of it, for the cloud. What is a Data Lake? A Data Lake is a location to store raw data that is in any format that an organization may produce or collect.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Lakes 63

Data Lakes AI AI Data Governance 63