Rockset

OCTOBER 11, 2022

Introduction Managing streaming data from a source system, like PostgreSQL, MongoDB or DynamoDB, into a downstream system for real-time analytics is a challenge for many teams. For a system like Elasticsearch , engineers need to have in-depth knowledge of the underlying architecture in order to efficiently ingest streaming data.

Data Ingestion 52

Data Ingestion Kafka Relational Database PostgreSQL 52

Ascend.io

MAY 31, 2023

Table of Contents The Common Threads: Ingest, Transform, Share Before we explore the differences between the ETL process and a data pipeline , let’s acknowledge their shared DNA. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Cloudera

AUGUST 13, 2021

Whether it is consuming log files, sensor metrics, and other unstructured data, most enterprises manage and deliver data to the data lake and leverage various applications like ETL tools, search engines, and databases for analysis. By modernizing the data flow, the enterprise got better insights into the business.

Data Pipeline 111

Data Pipeline Data Lake ETL Tools Unstructured Data 111

Knowledge Hut

JULY 6, 2023

IT Professionals looking to work in the cloud domain are expected to have a sound understanding of Azure tools as well as development and monitoring tools. Azure Monitoring Tools: Overview Microsoft Azure is the second-most popular cloud service provider, next to Amazon Web Service. Why are Azure Tools Important?

Amazon Web Services 52

Amazon Web Services Data Lake Java SQL 52

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value. ML workflow, ubr.to/3EJHjvm

Engineering 83

Engineering Raw Data Data Science Scala 83

Cloudera

JUNE 16, 2022

Faster data ingestion: streaming ingestion pipelines. Reduce ingest latency and complexity: Multiple point solutions were needed to move data from different data sources to downstream systems. Better yet, it works in any cloud environment. Not in the manufacturing space? Getting started today.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

Rockset

DECEMBER 9, 2021

Companies that undertook big data projects ran head-long into the high cost, rigidity and complexity of managing complex on-premises data stacks. Lifting-and-shifting their big data environment into the cloud only made things more complex. Every layer in the modern data stack was built for a batch-based world.

Transportation 52

Transportation BI SQL Data Warehouse 52

Rockset

FEBRUARY 14, 2022

That requires democratizing access to data, taking it from the C-suite and the data scientists training their ML models to every operational employee or customer who would stand to benefit. You can’t build a data-driven culture relying on batch-based analytics and BI. Not your customers, nor even your internal employees.

BI 52

BI Data Warehouse ETL Tools Data Lake 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. Delta Lake integrations.

Scala 64

Scala Data Lake BI Google Cloud 64

Towards Data Science

NOVEMBER 4, 2023

It simply makes sense to perform required data transformations, such as cleansing, enrichment and extraction in the place where data is being stored. Often it is a data warehouse solution (DWH) in the central part of our infrastructure. The downside of this approach is it’s pricing model though. ML model training using Airflow.

Data Engineering 79

Data Engineering Data Engineer Engineering BI 79