Snowflake

JUNE 28, 2023

To make it even easier to process data with Snowpark Python UDFs and Stored Procedures, we have added support for Python 3.9 and unstructured data support, now in public preview. Streamlit gives data scientists and Python developers the ability to quickly turn data and models into interactive, enterprise-ready applications.

Pipeline-centric 59

Pipeline-centric Programming Language Python Government 59

Snowflake

JUNE 28, 2023

Snowpark is our secure deployment and processing of non-SQL code, consisting of two layers: Familiar Client Side Libraries – Snowpark brings deeply integrated, DataFrame-style programming and OSS compatible APIs to the languages data practitioners like to use. Previously, tasks could be executed as quickly as 1-minute.

Python 52

Python Accessibility Accessible Pipeline-centric 52

Monte Carlo

JANUARY 23, 2024

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. Can I see the pipeline? Can I see the data source?’” John agrees. “

Database-centric 52

Database-centric Pipeline-centric Metadata Unstructured Data 52

Databand.ai

JULY 19, 2023

This helps to enhance data quality, facilitate data governance, and enable regulatory compliance. Notable Data Lineage Tools and Solutions Let’s take a look at several notable data lineage tools that can improve the quality and efficiency of your data pipeline.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Knowledge Hut

JANUARY 30, 2024

Structured Data: Structured data sources, such as databases and spreadsheets, often require extraction to consolidate, transform, and make them suitable for analysis. Unstructured Data: Unstructured data, like free-form text, can be challenging to work with but holds valuable insights.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

Knowledge Hut

SEPTEMBER 26, 2023

Key Features of Azure Synapse Here are some of the key features of Azure Synapse: Cloud Data Service: Azure Synapse operates as a cloud-native service, residing within the Microsoft Azure cloud ecosystem. This cloud-centric approach ensures scalability, flexibility, and cost-efficiency for your data workloads.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

ProjectPro

JUNE 1, 2017

Datos IO has extended its on-premise and public cloud data protection to RDBMS and Hadoop distributions. RecoverX is described as app-centric and can back up applications data whilst being capable of recovering it at various granularity levels to enhance storage efficiency. now provides hadoop support.

Hadoop 52

Hadoop Medical Pipeline-centric Database-centric 52

AltexSoft

JUNE 7, 2021

RDD easily handles both structured and unstructured data. It also provides tools for statistics, creating ML pipelines, model evaluation, and more. Spark core engine, data structures, and libraries are available via developer-friendly APIs. Its extension called DataSets merges benefits of the two previous models.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is the role of a Data Engineer? Data scientists and data Analysts depend on data engineers to build these data pipelines.

Data Engineer 98

Data Engineer Data Engineering Engineering Pipeline-centric 98

Edureka

JULY 4, 2024

Key Advantages of Azure Synapse No Code AI or Analytics Capabilities Azure Synapse takes a significant leap forward in democratizing data analytics and AI by offering robust no-code options. Lakehouse Architecture Pioneer Databricks brought the best elements of data lakes and data warehouses to create Lakehouse.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

Monte Carlo

JULY 15, 2021

As data pipelines become increasingly complex, investing in a data quality solution is becoming an increasingly important priority for modern data teams. There are 4 key challenges, opportunities, and trade-offs when considering building or buying a data observability or data quality solution.

Building 40

Building Pipeline-centric Data Engineer Data Engineering 40