DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. You can use it for big data analytics and machine learning workloads.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

IBM Big Data Hub

JUNE 15, 2023

The proliferation of data silos also inhibits the unification and enrichment of data which is essential to unlocking the new insights. Moreover, increased regulatory requirements make it harder for enterprises to democratize data access and scale the adoption of analytics and artificial intelligence (AI).

Data Warehouse 90

Data Warehouse Data Lake Optimization Data-driven 90

Cloudera

JUNE 30, 2022

These tools empower analysts and data scientists to easily collaborate on the same data, with their choice of tools and analytic engines. No more lock-in, unnecessary data transformations, or data movement across tools and clouds just to extract insights out of the data.

Data Lake 86

Data Lake Data Architecture Metadata Data Warehouse 86

Smarten

AUGUST 4, 2023

The Right Self-Serve Data Preparation Solution is Sophisticated, Easy-to-Use and Ensures User Adoption! When your enterprise decides to roll out analytics for business users, it is important to implement the right solution. Sophisticated Functionality – Don’t sacrifice functionality to get ease-of-use.

Data Lake 52

Data Lake Machine Learning Data Integration Optimization 52

IBM Big Data Hub

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data lakehouse was created to solve these problems.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

Cloudera

NOVEMBER 29, 2021

Data transforms businesses. That’s where the data lifecycle comes into play. Managing data and its flow, from the edge to the cloud, is one of the most important tasks in the process of gaining data intelligence. . The firm also worked on creating a solid pipeline from the data warehouse to the data lake.

Data Lake 77

Data Lake Data Warehouse Data Architecture Reporting 77

Cloudera

OCTOBER 7, 2022

Using these adapters, Cloudera customers can use dbt to collaborate, test, deploy, and document their data transformation and analytic pipelines on CDP Public Cloud, CDP One, and CDP Private Cloud. The Open Data Lakehouse . Cloudera builds dbt adaptors for all engines in the open data lakehouse. dbt-impala .

Data Warehouse 81

Data Warehouse Data Transformation Testing Data Lake 81

Ontotext

FEBRUARY 12, 2024

Transferring ownership of data/datasets to domain-specific units that possess a deeper understanding of rules around the data empowers teams, improves data quality and trust, and greatly accelerates the building of data models and analytics. However, data mesh is not about introducing new technologies.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

Cloudera

OCTOBER 11, 2021

Modak’s Nabu is a born in the cloud, cloud-neutral integrated data engineering platform designed to accelerate the journey of enterprises to the cloud. Modak empowers organizations to maximize their ROI from existing analytics infrastructure through interoperability. Modak Nabu TM and CDE’s Spark-on-Kubernetes.

Data Lake 92

Data Lake Cost-Benefit Data-driven Dashboards 92

Cloudera

OCTOBER 13, 2020

CDP Data Hub: a VM/Instance-based service that allows IT and developers to build custom business applications for a diverse set of use cases with secure, self-service access to enterprise data. . Enrich – Data Engineering (Apache Spark and Apache Hive). Predict – Data Engineering (Apache Spark). This is Now.

Data Warehouse 94

Data Warehouse Machine Learning Visualization Data Lake 94

IBM Big Data Hub

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. It enables secure data sharing for analytics and AI across your ecosystem.

Cost-Benefit 79

Cost-Benefit Metadata Optimization Management 79

IBM Big Data Hub

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? Capabilities within the Prompt Lab include: Summarize: Transform text with domain-specific content into personalized overviews and capture key points (e.g., AMC Networks is excited by the opportunity to capitalize on the value of all of their data to improve viewer experiences.

Machine Learning 65

Machine Learning Data Warehouse Modeling Cost-Benefit 65

AWS Big Data

MARCH 3, 2023

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. For Type , choose Spark.

Data Lake 86

Data Lake Dashboards Metrics Metadata 86

AWS Big Data

MAY 25, 2023

Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset.

Reporting 72

Reporting Metrics Optimization Data Lake 72

IBM Big Data Hub

OCTOBER 16, 2023

How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

Risk 73

Risk Modeling Management Metadata 73

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction. CRM platforms).

Data Processing 92

Data Processing Data Warehouse Enterprise Visualization 92

Alation

MARCH 22, 2022

Accenture calls it the Intelligent Data Foundation (IDF), and it’s used by dozens of enterprises with very complex data landscapes and analytic requirements. Simply put, IDF standardizes data engineering processes. They can better understand data transformations, checks, and normalization.

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

Cloudera

JUNE 1, 2021

Everyone from Snowflake and DataBricks to Google and Microsoft claim to have one, but the truth is that we are leading the way in the hybrid data cloud space. Integration between lifecycle analytic functions matters. The mission is to “Make data and analytics easy and accessible, for everyone.” Hybrid cloud matters.

Uncertainty 144

Uncertainty Cost-Benefit Risk Strategy 144

AWS Big Data

FEBRUARY 8, 2023

Amazon Redshift enables you to run complex SQL analytics at scale and performance on terabytes to petabytes of structured and unstructured data, and make the insights widely available through popular business intelligence (BI) and analytics tools. Answering questions as simple as “How many unique customers do we have?”

Data Quality 79

Data Quality Testing Data Warehouse Unstructured Data 79

Alation

AUGUST 11, 2022

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Subscribe to Alation's Blog.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. These two functions are nearly inseparable as we move further into a world of analytics that blends sources of varying volume, variety, veracity, and velocity. But this was only the tip of the analytics iceberg.

Modeling 84

Modeling Big Data IoT Data Warehouse 84