ODSC - Open Data Science

OCTOBER 13, 2024

Be sure to check out her talk, “ Don’t Go Over the Deep End: Building an Effective OSS Management Layer for Your Data Lake ,” there! Managing a data lake can often feel like being lost at sea — especially when dealing with both structured and unstructured data.

Data Lakes 40

Data Lakes Database Data Pipeline SQL 40

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

Women in Big Data

OCTOBER 9, 2024

The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline? A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

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 Data Science 98

Pickl AI

APRIL 21, 2025

Key Takeaways Big Data focuses on collecting, storing, and managing massive datasets. Data Science extracts insights and builds predictive models from processed data. Big Data technologies include Hadoop, Spark, and NoSQL databases. Data Science uses Python, R, and machine learning frameworks.

Big Data 52

Big Data Big Data Data Science Machine Learning 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 130

Analytics Analytics Data Warehouse Business Intelligence 130

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

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

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

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. This section explores essential aspects of Data Engineering.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

AWS Machine Learning Blog

JUNE 18, 2024

The solution harnesses the capabilities of generative AI, specifically Large Language Models (LLMs), to address the challenges posed by diverse sensor data and automatically generate Python functions based on various data formats. This allows for data to be aggregated for further manufacturer-agnostic analysis.

AWS 132

AWS Python AI AI 132

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

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 123

ML ML AWS Data Warehouse 123

The MLOps Blog

JANUARY 23, 2023

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. First of all, machine learning engineers and data scientists often use data from different data vendors.

ML 52

ML ML Data Lakes Machine Learning 52

The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. 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 Pipeline 59

Data Pipeline ETL SQL Data Quality 59

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 152

ML ML AWS AI 152

Women in Big Data

NOVEMBER 27, 2024

A data warehouse is a centralized repository designed to store and manage vast amounts of structured and semi-structured data from multiple sources, facilitating efficient reporting and analysis. architecture for both structured and unstructured data.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

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 94

Data Science Analytics Analytics Data Scientist 94

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.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date. mp4,webm, etc.), and audio files (.wav,mp3,acc,

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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 73

Data Lakes AI AI Data Governance 73

Pickl AI

JULY 25, 2024

It’s the critical process of capturing, transforming, and loading data into a centralised repository where it can be processed, analysed, and leveraged. Data Ingestion Meaning At its core, It refers to the act of absorbing data from multiple sources and transporting it to a destination, such as a database, data warehouse, or data lake.

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

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.

Database 98

Database Clustering SQL Data Pipeline 98

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

Pickl AI

JULY 30, 2024

Flow-Based Programming : NiFi employs a flow-based programming model, allowing users to create complex data flows using simple drag-and-drop operations. This visual representation simplifies the design and management of data pipelines. Its visual interface allows users to design complex ETL workflows with ease.

ETL 52

ETL Data Lakes Big Data Big Data 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 Big Data 52

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

Pickl AI

APRIL 9, 2025

The importance of ETL tools is underscored by their ability to handle diverse data sources, from relational databases to cloud-based services. This capability allows organizations to consolidate disparate data into a unified repository for analytics and reporting, providing insights that can drive strategic decisions.

ETL 52

ETL Data Warehouse AWS Business Intelligence 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

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

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

Alation

APRIL 19, 2022

Data producers and consumers alike are working from home and hybrid locations more often. And in an increasingly remote workforce, people need to access data systems easily to do their jobs. This might mean that they’re accessing a database from a smartphone, computer, or tablet. Today, data dwells everywhere.

Data Governance 52

Data Governance Cloud Data Database Data Silos 52

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.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

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 88

AI AI Data Warehouse ML 88

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 134

AWS ML ML Algorithm 134

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