Rockset

FEBRUARY 9, 2021

Because it integrates easily with S3, is serverless, and uses a familiar language, Athena has become the default service for most business intelligence (BI) decision makers to query the large amounts of (usually streaming) data coming into their object stores.

Data Lake 40

Data Lake Business Intelligence SQL Datasets 40

Cloudera

JUNE 16, 2022

Building real-time data analytics pipelines is a complex problem, and we saw customers struggle using processing frameworks such as Apache Storm, Spark Streaming, and Kafka Streams. . Without context, streaming data is useless.”

Kafka 88

Kafka Manufacturing Data Lake SQL 88

Cloudera

DECEMBER 1, 2022

Full-stack observability is a critical requirement for effective modern data platforms to deliver the agile, flexible, and cost-effective environment organizations are looking for. For example, historically the process of acquiring data from the source systems to populate the data lake was plagued by schema drift.

Data Lake 84

Data Lake Data Pipeline Analytics Application Data Governance 84

Monte Carlo

MAY 14, 2024

It enhances performance specifically for large-scale data processing tasks, offering advanced optimizations for superior data compression and fast data scans, essential in data warehousing and analytics applications. For example, Starburst’s Icehouse implementation pairs Iceberg with open query engine Trino.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. CRM platforms). benchmarking study conducted by independent 3rd party ).

Hadoop 94

Hadoop Government Data Security Cloud 94

Edureka

JULY 3, 2024

ADF leverages compute services like Azure HDInsight, Spark, Azure Data Lake Analytics, or Machine Learning to process and analyze the data according to defined requirements. Publish: Transformed data is then published either back to on-premises sources like SQL Server or kept in cloud storage.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Cloudera

FEBRUARY 8, 2024

The support for Apache Iceberg as the table format in Cloudera Data Platform and the ability to create and use materialized views on top of such tables provides a powerful combination to build fast analytic applications on open data lake architectures.

Metadata 91

Metadata Data Warehouse BI AWS 91

ProjectPro

MARCH 1, 2017

Two Tech giants, Hortonworks and IBM have partnered to enable IBM clients run hadoop analytics directly on IBM storage without requiring a separate analytic storage.IBM’s enterprise storage will be paired with Hortonworks analytics application so that clients can opt for either centralized or distributed deployments.

Hadoop 40

Hadoop Food Data Lake Banking 40

Cloudera

MAY 16, 2018

However, in this case, that output is ingested into a data lake. Instead of each group’s tools acting on the output in isolation, they leverage a common visual analytics platform that is native to the lake and uses all of the data without moving it to a separate server. Going Forward: Improved Economics.

Data Lake 42

Data Lake Electronics Media Unstructured Data 42

ProjectPro

SEPTEMBER 9, 2016

HCL employs a simple and intuitive assessment to identify the big data maturity of the customer and suggest appropriate course of action to leverage maximum potential of big data.

Hadoop 40

Hadoop Data Lake Big Data Cloud Computing 40

Towards Data Science

APRIL 3, 2023

From Enormous Data back to Big Data Say you are tasked with building an analytics application that must process around 1 billion events (1,000,000,000) a day. For example, custom reporting jobs and exploratory data analysis are two styles of data access that lend themselves nicely to these paradigms.

Process 98

Process Data Lake Systems Data Engineering 98

Grouparoo

NOVEMBER 16, 2021

The critical benefit of transformation is that it allows analytical applications to efficiently access and process all data quickly and efficiently by eliminating issues before processing. An added benefit is that transformation to a standard format will make the manual inspection of data more convenient.

Data Mining 52

Data Mining Raw Data ETL Tools Data 52

Rockset

AUGUST 18, 2020

Analysts predict that by 2025 more than 30% of data will be real-time in nature, and by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

Data Lake 40

Data Lake Data Schemas BI Kafka 40

Striim

AUGUST 31, 2023

Treating batch and streaming as separate pipelines for separate use cases drives up complexity, cost, and ultimately deters data teams from solving business problems that truly require data streaming architectures. Finally, kappa architectures are not suitable for all types of data processing tasks.

Data Integration 69

Data Integration Architecture Amazon Web Services Machine Learning 69

Rockset

DECEMBER 9, 2019

Variety One of the biggest advancements in recent years in regards to data platforms is the ability to extract data from storage silos and into a data lake. This obviously introduces a number of problems for businesses who want to make sense of this data because it’s now arriving in a variety of formats and speeds.

Data Engineering 40

Data Engineering Data Engineer Engineering Raw Data 40

Knowledge Hut

JUNE 26, 2023

The incoming data would be analogous to an event that occurred when a person listened to music, navigated around the website, or authenticated themselves. The processing of the data would take place in real-time, and it would be saved to the data lake at regular intervals (every two minutes).

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Rockset

JULY 6, 2022

This radical design choice made NoSQL databases — document databases, key-value stores, column-oriented databases and graph databases — great at storing huge amounts of data of varying kinds together, whether it is structured, semi-structured or polymorphic.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Rockset

AUGUST 4, 2021

Using minutes- and seconds-old data for real-time personalization has always been elusive but can significantly grow user engagement. Operational Analytics Applications such as e-commerce, gaming, and the Internet of things (IoT) commonly require real-time views of what’s happening on a site, in a game, or at a manufacturing plant.

Data Ingestion 40

Data Ingestion Cloud Storage Data Warehouse Architecture 40

Knowledge Hut

JUNE 30, 2023

Key Benefits and Takeaways: Understand data intake strategies and data transformation procedures by learning data engineering principles with Python. Investigate alternative data storage solutions, such as databases and data lakes.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Rockset

MARCH 19, 2020

Intro In recent years, Kafka has become synonymous with “streaming,” and with features like Kafka Streams, KSQL, joins, and integrations into sinks like Elasticsearch and Druid, there are more ways than ever to build a real-time analytics application around streaming data in Kafka. Postgres), and maybe even data lake (i.e.

Kafka 40

Kafka Database Process SQL 40

ProjectPro

FEBRUARY 16, 2023

SQL in Big Data SQL is not just limited to data warehousing and traditional relational database management systems (RDBMS). To analyze big data and create data lakes and data warehouses , SQL-on-Hadoop engines run on top of distributed file systems.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

ProjectPro

MAY 23, 2015

During this program the candidates are required to spend some time with the different departments in the company to understand how big data analytics is being leveraged across the company. Walmart has signed a five-year deal with Microsoft and turned to Azure cloud services.

Big Data 52

Big Data Data Analysis Hadoop Retail 52

ProjectPro

JANUARY 31, 2023

It also performs better when dealing with large amounts of data since it can quickly scale up and down according to your needs. Finally, NoSQL databases are frequently used in real-time analytics applications, such as streaming data from IoT sensors. It works with AWS analytics services as well as Amazon S3 data lakes.

Big Data 52

Big Data Hadoop Relational Database AWS 52