Flipboard

NOVEMBER 27, 2024

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis.

ETL 138

ETL Data Warehouse Analytics Analytics 138

Pickl AI

FEBRUARY 23, 2023

Organisations must store data in a safe and secure place for which Databases and Data warehouses are essential. You must be familiar with the terms, but Database and Data Warehouse have some significant differences while being equally crucial for businesses. What is Data Warehouse?

Data Warehouse 52

Data Warehouse Database Data Analysis Data Analysis 52

Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

Data Lakes 59

Data Lakes Data Warehouse Data Quality Data Governance 59

Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

Tableau 101

Tableau Data Lakes Data Warehouse SQL 101

Smart Data Collective

JUNE 15, 2021

It’s also possible to employ extra caching or materialized views in the data warehouse in addition to caching in Looker (depending on the capability of your data warehouse). One added tip is to aggregate your data before loading it into Looker or in the data warehouse to reduce the amount of data loaded onto the platform.

Data Warehouse 101

Data Warehouse Database SQL Data Analyst 101

Data Science 101

NOVEMBER 7, 2019

Microsoft just held one of its largest conferences of the year, and a few major announcements were made which pertain to the cloud data science world. Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Those are the big data science announcements of the week.

Data Science 106

Data Science Azure SQL Machine Learning 106

Dataconomy

SEPTEMBER 4, 2023

Big data analytics: Big data analytics is designed to handle massive volumes of data from various sources, including structured and unstructured data. Big data analytics is essential for organizations dealing with large-scale data, such as social media platforms, e-commerce giants, and scientific research.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Big data analytics from 2022 show a dramatic surge in information consumption.

Big Data 117

Big Data Big Data Data Engineer Data Engineering 117

Dataconomy

SEPTEMBER 27, 2024

This experience helped me to improve my Python skills and get more practical experience working with big data. I am also a judge in several international hackathons, including the United Nations Big Data Hackathon , where I evaluated 18 different teams based on their solutions’ innovation, quality, and applicability.

Analytics 113

Analytics Analytics Big Data Big Data 113

phData

AUGUST 9, 2023

In short, ELT exemplifies the data strategy required in the era of big data, cloud, and agile analytics. With ELT, we first extract data from source systems, then load the raw data directly into the data warehouse before finally applying transformations natively within the data warehouse.

ETL 52

ETL Data Warehouse Cloud Data Big Data 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. Data orchestration tools. How Did the Modern Data Stack Get Started? Reverse ETL tools.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Smart Data Collective

APRIL 29, 2020

Usually the term refers to the practices, techniques and tools that allow access and delivery through different fields and data structures in an organisation. Data management approaches are varied and may be categorised in the following: Cloud data management. Master data management. Data transformation.

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Data Warehouse Azure SQL ETL 88

IBM Journey to AI blog

JULY 11, 2023

Data warehouses are a critical component of any organization’s technology ecosystem. The next generation of IBM Db2 Warehouse brings a host of new capabilities that add cloud object storage support with advanced caching to deliver 4x faster query performance than previously, while cutting storage costs by 34x 1.

Data Warehouse 60

Data Warehouse Database Cloud Data Business Intelligence 60

Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

Alation

JULY 30, 2021

A part of that journey often involves moving fragmented on-premises data to a cloud data warehouse. You clearly shouldn’t move everything from your on-premises data warehouses. Otherwise, you can end up with a data swamp. Put differently, you can migrate faster if you know the right data to move.

Data Warehouse 52

Data Warehouse Data Governance Cloud Data Machine Learning 52

phData

OCTOBER 9, 2023

As organizations embrace the benefits of data vault, it becomes crucial to ensure optimal performance in the underlying data platform. One such platform that has revolutionized cloud data warehousing is the Snowflake Data Cloud. How do I build a data vault?

ETL 69

ETL Clustering Data Warehouse SQL 69

phData

NOVEMBER 8, 2024

Versioning also ensures a safer experimentation environment, where data scientists can test new models or hypotheses on historical data snapshots without impacting live data. Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. Contact phData Today!

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

phData

MAY 4, 2023

To date, the company’s data warehousing solutions are largely built from the same template used in 1979. In short, they are still the model of multiple processors and massive disk storage with data warehouse software on the top layer managing it all. What Are The Benefits Of Moving To Snowflake?

SQL 52

SQL Data Warehouse Azure Big Data 52

phData

JULY 10, 2024

Unlike traditional BI tools, its user-friendly interface ensures that users of all technical levels can seamlessly interact with data. The platform’s integration with cloud data warehouses like Snowflake AI Data Cloud , Google BigQuery, and Amazon Redshift makes it a vital tool for organizations harnessing big data.

SQL 52

SQL Data Warehouse Data Analysis Data Analysis 52

Precisely

APRIL 3, 2023

It simply wasn’t practical to adopt an approach in which all of an organization’s data would be made available in one central location, for all-purpose business analytics. To speed analytics, data scientists implemented pre-processing functions to aggregate, sort, and manage the most important elements of the data.

ETL 52

ETL Analytics Analytics Database 52

Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.

Data Governance 59

Data Governance ML ML Cloud Data 59

Precisely

MAY 13, 2024

As enterprise technology landscapes grow more complex, the role of data integration is more critical than ever before. Wide support for enterprise-grade sources and targets Large organizations with complex IT landscapes must have the capability to easily connect to a wide variety of data sources.

Data Governance 52

Data Governance Data Pipeline Cloud Data Data Quality 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. link] Tables The table in GCP BigQuery is a collection of rows and columns that can store and manage massive amounts of data.

SQL 52

SQL Database Apache Hadoop Data Science 52

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

Analytics Analytics Data Silos Data Lakes 69

Alation

JANUARY 25, 2022

On the policy front, a feature like Policy Center empowers users to enforce and track policies at scale; this ensures that people use data compliantly, and organizations are prepared for compliance audits. How can data users navigate and understand such a complex landscape predictably?

DataOps 52

DataOps SQL ML ML 52

Women in Big Data

DECEMBER 9, 2024

Introduction In the rapidly evolving landscape of data analytics, Business Intelligence (BI) tools have become indispensable for organizations seeking to leverage their big data stores for strategic decision-making. Tableau – Tableau is celebrated for its advanced data visualization and interactive dashboard features.

Big Data 52

Big Data Big Data Power BI Tableau 52

IBM Journey to AI blog

JUNE 15, 2023

It is comprised of commodity cloud object storage, open data and open table formats, and high-performance open-source query engines. To help organizations scale AI workloads, we recently announced IBM watsonx.data , a data store built on an open data lakehouse architecture and part of the watsonx AI and data platform.

Data Warehouse 110

Data Warehouse Data Lakes Cloud Data Analytics 110

phData

APRIL 20, 2023

In today’s world, data-driven applications demand more flexibility, scalability, and auditability, which traditional data warehouses and modeling approaches lack. This is where the Snowflake Data Cloud and data vault modeling comes in handy. What is Data Vault Modeling?

Data Warehouse 72

Data Warehouse Data Governance Clustering Database 72

IBM Journey to AI blog

AUGUST 3, 2023

IBM Security® Guardium® Data Protection empowers security teams to safeguard sensitive data through discovery and classification, data activity monitoring, vulnerability assessments and advanced threat detection.

Azure 71

Azure Database Analytics Analytics 71