Monte Carlo

APRIL 24, 2023

Sometimes the build versus buy debate can include layers of its own, with decisions to buy often evolving into a decision to use open-source tooling to retain flexibility or leverage fully-managed solutions for faster time-to-value. Missed Nishith’s 5 considerations? Check out Part 1 of the build vs buy guide to catch up.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Monte Carlo

AUGUST 19, 2023

By integrating tools from a variety of vendors, a data platform enables a data engineering team to not only manage an organization’s data but also activate it for a domain’s use cases. From data sources and use cases to industry and company size, how a company structures its data platform will depend on a variety of critical factors.

Building 52

Building BI Data Lake Data Governance 52

Ascend.io

MAY 19, 2023

In the data world, this disruption manifested in the form of cloud computing with technologies such as Redshift, Snowflake, and Spark. In our case, data ingestion, transformation, orchestration, reverse ETL, and observability. And the innovators were the point solutions created to leverage its intrinsic capacity and scalability.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Monte Carlo

MAY 17, 2023

Because the technology is so extensible, there have been a wide array of suggestions–some more grounded than others–for how it can be used. To help clarify, we reviewed hundreds of data observability deployments to identify 61 real data observability use cases and benefits. Table of Contents Core Data Observability Use Cases 1.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Because the technology is so extensible, there have been a wide array of suggestions–some more grounded than others–for how it can be used. To help clarify, we reviewed hundreds of data observability deployments to identify 61 real data observability use cases and benefits. Table of Contents Core Data Observability Use Cases 1.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52

AltexSoft

JULY 18, 2023

With its native support for in-memory distributed processing and fault tolerance, Spark empowers users to build complex, multi-stage data pipelines with relative ease and efficiency. What Apache Spark is used for With its diverse capabilities, Apache Spark is used in various domains. Here are some of the possible use cases.

Big Data 52

Big Data Data Process Process Hadoop 52

phData: Data Engineering

JANUARY 3, 2022

If you have hundreds of thousands of users using a variety of products, you need more tooling and processes at your disposal that enables a thoughtful and scalable approach. Most companies begin by using Microsoft Excel , downloading CSV files from a variety of sources in order to clean data, perform analytics, and generate reports.

IT 52

IT AWS Software Engineer Software Engineering 52