IBM Journey to AI blog

MARCH 14, 2024

Db2 Warehouse fully supports open formats such as Parquet, Avro, ORC and Iceberg table format to share data and extract new insights across teams without duplication or additional extract, transform, load (ETL). This allows you to scale all analytics and AI workloads across the enterprise with trusted data. 

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Smart Data Collective

OCTOBER 17, 2022

All of this data might be overwhelming for engineers who struggle to pull in data sets quickly enough. Older ETL technology, which might be code-heavy and slow down your process even more, isn’t helpful. Other industries fear automation, but data engineers are their friends in this instance.

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Big Data Big Data Data Engineering Data Engineering 119

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. The good news is that there are many skills that data scientists already have that are transferable to data engineering.

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Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. A Note on the Shift from ETL to ELT. Data scientists.

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Data Warehouse ETL Tableau Cloud Data 52

IBM Journey to AI blog

AUGUST 4, 2023

When done well, data democratization empowers employees with tools that let everyone work with data, not just the data scientists. When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?

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Data Lakes AI AI Data Governance 73

Pickl AI

NOVEMBER 15, 2023

Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible. Importance of Data Lakes Data Lakes play a pivotal role in modern data analytics, providing a platform for Data Scientists and analysts to extract valuable insights from diverse data sources.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

AWS Machine Learning Blog

AUGUST 21, 2023

We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction. In this post, we show how to use Lake Formation as a central data governance capability and Amazon EMR as a big data query engine to enable access for SageMaker Data Wrangler.

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AWS Data Lakes Clustering Data Preparation 98

Precisely

MARCH 9, 2023

Raw Data Data warehouses emerged several decades ago as a means of combining, harmonizing, and preprocessing data in preparation for advanced analytics. A data warehouse implies a certain degree of preprocessing, or at the very least, an organized and well-defined data model.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

The MLOps Blog

MARCH 15, 2023

Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. Data governance: Ensure that the data used to train and test the model, as well as any new data used for prediction, is properly governed.

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AWS ETL ML ML 52

phData

APRIL 18, 2023

Snowflake enables organizations to instantaneously scale to meet SLAs with timely delivery of regulatory obligations like SEC Filings, MiFID II, Dodd-Frank, FRTB, or Basel III—all with a single copy of data enabled by data sharing capabilities across various internal departments.

Data Silos 52

Data Silos ETL Clustering Analytics 52

Alation

OCTOBER 27, 2022

We specialize in multiple functions, which include but are not limited to, data governance , dashboarding, data & analytics engineering, and data science. At Alation, we focus most of our time on connecting data sources and building useful data transformations to provide reporting for different teams.

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

Precisely

JUNE 6, 2023

Salam noted that organizations are offloading computational horsepower and data from on-premises infrastructure to the cloud. This provides developers, engineers, data scientists and leaders with the opportunity to more easily experiment with new data practices such as zero-ETL or technologies like AI/ML.

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Data Observability Data Lakes Data Quality ETL 52

phData

SEPTEMBER 4, 2024

ThoughSpot can easily connect to top cloud data platforms such as Snowflake AI Data Cloud , Oracle, SAP HANA, and Google BigQuery. In that case, ThoughtSpot also leverages ELT/ETL tools and Mode, a code-first AI-powered data solution that gives data teams everything they need to go from raw data to the modern BI stack.

Analytics 52

Analytics Analytics SQL ETL 52

Pickl AI

DECEMBER 10, 2024

A unified data fabric also enhances data security by enabling centralised governance and compliance management across all platforms. Automated Data Integration and ETL Tools The rise of no-code and low-code tools is transforming data integration and Extract, Transform, and Load (ETL) processes.

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Analytics Analytics Augmented Analytics Machine Learning 52

phData

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? A data warehouse is a centralised repository that consolidates data from various sources for reporting and analysis. It is essential to provide a unified data view and enable business intelligence and analytics.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

AUGUST 9, 2024

Data Integration Tools Technologies such as Apache NiFi and Talend help in the seamless integration of data from various sources into a unified system for analysis. Understanding ETL (Extract, Transform, Load) processes is vital for students. Understanding how to assess model performance is crucial for data scientists.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

phData

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data. However, merely knowing what it consists of isn’t enough.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Alation

APRIL 14, 2023

Last week, the Alation team had the privilege of joining IT professionals, business leaders, and data analysts and scientists for the Modern Data Stack Conference in San Francisco. In this blog, I’ll share a quick high-level overview of the event, with an eye to core themes. Let’s dive in!

Data Pipeline 52

Data Pipeline Data Warehouse Cloud Data ETL 52

phData

SEPTEMBER 27, 2024

If the event log is your customer’s diary, think of persistent staging as their scrapbook – a place where raw customer data is collected, organized, and kept for future reference. In traditional ETL (Extract, Transform, Load) processes in CDPs, staging areas were often temporary holding pens for data.

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Data Modeling Data Models Apache Kafka Data Lakes 52