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

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 Engineering Data Engineering Data Engineer 52

Pickl AI

JULY 30, 2024

Flexibility : NiFi supports a wide range of data sources and formats, allowing organizations to integrate diverse systems and applications seamlessly. Scalability : NiFi can be deployed in a clustered environment, enabling organizations to scale their data processing capabilities as their data needs grow.

ETL 52

ETL Data Lakes Big Data Big Data 52

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

JULY 1, 2024

Data lineage is the discipline of understanding how data flows through your organization: where it comes from, where it goes, and what happens to it along the way. Often used in support of regulatory compliance, data governance and technical impact analysis, data lineage answers these questions and more.

ETL 40

ETL Data Lakes Database Data Pipeline 40

Pickl AI

AUGUST 9, 2024

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

APRIL 26, 2024

I would perform exploratory data analysis to understand the distribution of customer transactions and identify potential segments. Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. What approach would you take?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

DagsHub

APRIL 7, 2024

Flexibility: Its use cases are wider than just machine learning; for example, we can use it to set up ETL pipelines. Cloud-agnostic and can run on any Kubernetes cluster. Integration: It can work alongside other workflow orchestration tools (Airflow cluster or AWS SageMaker Pipelines, etc.)

Machine Learning 59

Machine Learning Machine Learning ML ML 59

phData

AUGUST 10, 2023

In data vault implementations, critical components encompass the storage layer, ELT technology, integration platforms, data observability tools, Business Intelligence and Analytics tools, Data Governance , and Metadata Management solutions. could be considered to automate data vault design and development.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

DagsHub

OCTOBER 23, 2024

Kafka is highly scalable and ideal for high-throughput and low-latency data pipeline applications. Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers. is similar to the traditional Extract, Transform, Load (ETL) process. Unstructured.io

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

SQL 139

SQL Data Analyst Data Warehouse AWS 139

databricks

JUNE 16, 2025

Enterprises want to apply fine-grained access control policies to manage increasingly complex data governance requirements. Second, it leverages container isolation in Databricks’ cluster manager to securely isolate user code from the core Spark engine. These rich policies should be uniformly applied across all their workloads.

Data Science 130

Data Science Artificial Intelligence Business Intelligence Business Intelligence 130