Striim

NOVEMBER 8, 2023

A data mesh is technology-agnostic and underpins four main principles described in-depth in this blog post by Zhamak Dehghani. The four data mesh principles aim to solve major difficulties that have plagued data and analytics applications for a long time.

Architecture 52

Architecture Generalist Government Datasets 52

Cloudera

AUGUST 13, 2021

Rather than collecting every single event and analyzing later, it would make sense to identify the important data as it is being collected. Let’s transform the first mile of the data pipeline. They also reduced terabytes of data ingestion, which significantly brought down the infrastructure and licensing costs by 30%.

Data Pipeline 109

Data Pipeline Data Lake ETL Tools Unstructured Data 109

Knowledge Hut

JUNE 16, 2023

Data Engineer Data engineers are engineers responsible for creating reliable architecture and interface designs to collect and transform data from various sources. regularly hire Data Engineers in India.

Data Analytics 52

Data Analytics Business Analyst Business Intelligence Consulting 52

Rockset

FEBRUARY 2, 2022

Let’s explore five ways to run MongoDB analytics, along with the pros and cons of each method. 1 – Query MongoDB Directly The first and most direct approach is to run your analytical queries directly against MongoDB. Data warehouses have a heavy reliance on scans, which increases query latency.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

Data Engineering Podcast

MARCH 3, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. What are the key considerations for powering AI applications that are substantially different from analytical applications?

Programming 162

Programming Data Lake High Quality Data Data Pipeline 162

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. How you transition from a batch mindset to a streaming mindset although can also be tricky, so let’s start small and build.

Process 87

Process Data Lake Systems Bytes 87

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.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

ProjectPro

FEBRUARY 16, 2023

Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. 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

Workfall

JUNE 27, 2023

In this blog, we will cover: Apache Kafka Powerful Features of Apache Kafka 5 Key Apache Kafka Use Cases in 2023 Apache Kafka in Microservices Hands-on Conclusion Apache Kafka Apache Kafka is a distributed data stream platform that aims at having unified, high-throughput data pipelines.

Kafka 75

Kafka Architecture AWS Transportation 75

Rockset

JUNE 21, 2022

We’re excited to announce that Rockset’s new connector with Snowflake is now available and can increase cost efficiencies for customers building real-time analytics applications. Rockset efficiently organizes data in a Converged Index ™, which is optimized for real-time data ingestion and low-latency analytical queries.

Kafka 52

Kafka Data Warehouse BI Analytics Application 52

Rockset

MARCH 15, 2021

This includes making the data available for query as soon as it is ingested, creating proper indexes on the data so that the query latency is very low, and much more. Before it can be ingested, there’s usually a data pipeline for transforming incoming data. latency, indexing, etc.),

MongoDB 40

MongoDB Data Ingestion Analytics Application Kafka 40

Striim

AUGUST 31, 2023

This makes scaling the architecture complex and costly, as businesses will need to invest in additional hardware or cloud computing services in order to handle larger volumes of data processing. Finally, kappa architectures are not suitable for all types of data processing tasks.

Data Integration 69

Data Integration Architecture Amazon Web Services ETL System 69

Rockset

FEBRUARY 24, 2023

It has expanded to various industries and applications, including IoT sensor data, financial data, web analytics, gaming behavioral data, and many more use cases. It supports various data processing models such as stream and batch processing (both covered in part 2 of this series), and complex event processing.

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Towards Data Science

APRIL 26, 2023

Whether you work in BI, Data Science or ML all that matters is the final application and how fast you can see it working end-to-end. Imagine, as a practical example, that we need to build a new customer-facing analytics application for our product team. The infrastructure often gets in the way though.

Engineering 70

Engineering Data Lake AWS BI 70

Rockset

FEBRUARY 25, 2021

A lot of data systems that provide real-time analytics require non-trivial ETL (extract, transform, load) to get the data into the “right” shape, or may not provide the analytical functionality required by the application. Let’s look at how Elasticsearch and Rockset stack up with these considerations in mind.

SQL 40

SQL Data Pipeline Kafka Database 40

Cloudera

NOVEMBER 13, 2017

Cloud PaaS takes this a step further and allows users to focus directly on building data pipelines, training machine learning models, developing analytics applications — all the value creation efforts, vs the infrastructure operations.

Cloud 40

Cloud Big Data Metadata Machine Learning 40

Cloudera

DECEMBER 7, 2017

Cloud makes it fast and easy to spin up resources for new applications. Cloud offers elasticity of those resources to efficiently support transient analytics workloads and data pipelines. Cloud offers self-service without waiting for IT infrastructure and operations teams.

Building 40

Building Management Government BI 40

AltexSoft

OCTOBER 21, 2022

However, there is a range of open-source client libraries enabling you to build Kafka data pipelines with practically any popular programming language or framework. Kafka is complex in terms of cluster setup and configuration, maintenance, and data pipeline design. In former times, Kafka worked with Java only.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

AltexSoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.

Big Data 52

Big Data Data Process Process Hadoop 52

Rockset

DECEMBER 9, 2019

It continuously ingests raw data from multiple sources--data lakes, data streams, databases--into its storage layer and allows fast SQL access from both visualisation tools and analytic applications.

Data Engineering 40

Data Engineering Data Engineer Raw Data Engineering 40

ProjectPro

SEPTEMBER 6, 2021

Popular instances where GCP is used widely are machine learning analytics, application modernization, security, and business collaboration. It is a serverless data integration service that makes data preparation easier, cheaper and faster.

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 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. Multiple users cannot simultaneously write to the same HDFS file.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Cloudera

JUNE 16, 2022

CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. We had to build the streaming data pipeline that new data has to move through before it can be persisted and then provide business teams access to that pipeline for them to build data products.”

Kafka 88

Kafka Manufacturing Data Lake SQL 88

Cloudera

OCTOBER 12, 2020

Eventador simplifies the process by allowing users to use SQL to query streams of real-time data without implementing complex code. We believe Eventador will accelerate innovation in our Cloudera DataFlow streaming platform and deliver more business value to our customers in their real-time analytics applications.

Cloud 131

Cloud Process Scala Kafka 131

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications.

Big Data 52

Big Data Coding Project Hadoop 52

Rockset

DECEMBER 9, 2021

Moreover, new fields and columns of data are constantly appearing. These can easily break rigid data pipelines in the batch world. Destination: Data Apps and Microservices. Real-time data streams typically power analytical or data applications whereas batch systems were built to power static dashboards.

Transportation 52

Transportation BI SQL Data Warehouse 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 ).

Government 94

Government Hadoop Data Security Data Warehouse 94

phData: Data Engineering

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52