Rockset

MAY 3, 2023

lower latency than Elasticsearch for streaming data ingestion. In this blog, we’ll walk through the benchmark framework, configuration and results. We’ll also delve under the hood of the two databases to better understand why their performance differs when it comes to search and analytics on high-velocity data streams.

Data Ingestion 59

Data Ingestion Kafka Database Architecture 59

Pinterest Engineering

SEPTEMBER 12, 2023

transformers) became standardized, ML engineers started to show a growing appetite to iterate on datasets. While such dataset iterations can yield significant gains, we observed that only a handful of such experiments were conducted and productionized in the last six months. As model architecture building blocks (e.g.

Data Process 103

Data Process Process Datasets Software Engineer 103

Cloudera

DECEMBER 14, 2021

So to improve the speed of data analysis, the IRS worked with the combined technology integrating Cloudera Data Platform (CDP) and NVIDIA’s RAPIDS Accelerator for Apache Spark 3.0. The Roads and Transport Authority (RTA) operating in Dubai wanted to apply big data capabilities to transportation and enhance travel efficiency.

Transportation 114

Transportation Telecommunication Banking Data Lake 114

Cloudera

JANUARY 20, 2021

The missing chapter is not about point solutions or the maturity journey of use cases, the missing chapter is about the data, it’s always been about the data, and most importantly the journey data weaves from edge to artificial intelligence insight. . Conclusion.

Manufacturing 112

Manufacturing Data Warehouse Kafka Retail 112

Cloudera

NOVEMBER 17, 2020

Once the prototype has been completely deployed, you will have an application that is able to make predictions to classify transactions as fraudulent or not: The data for this is the widely used credit card fraud dataset. Data analysis – create a plan to build the model.

Deep Learning 117

Deep Learning Machine Learning Raw Data Data Ingestion 117

Lyft Engineering

NOVEMBER 29, 2023

In this particular blog post, we explain how Druid has been used at Lyft and what led us to adopt ClickHouse for our sub-second analytic system. Druid at Lyft Apache Druid is an in-memory, columnar, distributed, open-source data store designed for sub-second queries on real-time and historical data.

Kafka 104

Kafka Data Ingestion Datasets Architecture 104

DataKitchen

NOVEMBER 5, 2024

Finally, the challenge we are addressing in this document – is how to prove the data is correct at each layer.? How do you ensure data quality in every layer? The Medallion architecture is a framework that allows data engineers to build organized and analysis-ready datasets in a lakehouse environment.

Architecture 81

Architecture Raw Data Pipeline-centric Data Ingestion 81

Confluent

FEBRUARY 6, 2019

The blog posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable and mission-critical nervous system. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Python Kafka Java 107

Data Engineering Weekly

SEPTEMBER 18, 2024

Data testing checks for rule-based validations, while observability ensures overall pipeline health, tracking aspects like latency, freshness, and lineage. How to Evaluate a Data Observability Tool When selecting a data observability tool, assessing both functionality and how well it integrates into your existing data stack is important.

Data Lake 98

Data Lake Data Pipeline Unstructured Data Datasets 98

DataKitchen

MAY 10, 2024

Harnessing Data Observability Across Five Key Use Cases The ability to monitor, validate, and ensure data accuracy across its lifecycle is not just a luxury—it’s a necessity. Data Evaluation Before new data sets are introduced into production environments, they must be thoroughly evaluated and cleaned.

Data Ingestion 60

Data Ingestion Datasets Data Coding 60

Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines.

Machine Learning 98

Machine Learning Manufacturing Data Collection Data Science 98

Snowflake

APRIL 18, 2024

This blog post explores how Snowflake can help with this challenge. Legacy SIEM cost factors to keep in mind Data ingestion: Traditional SIEMs often impose limits to data ingestion and data retention. Security teams can also reduce their costs by loading certain datasets in batches instead of continuously.

Data Lake 85

Data Lake Data Ingestion Bytes Cloud Computing 85

Confluent

JULY 3, 2019

From a data perspective, the World Cup represents an interesting source of information. The idea in this blog post is to mix information coming from two distinct channels: the RSS feeds of sport-related newspapers and Twitter feeds of the FIFA Women’s World Cup. Ingesting Twitter data.

Data Analysis 21

Data Analysis Kafka Datasets Java 21

Confluent

SEPTEMBER 26, 2019

In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another data lake. ® , Go, and Python SDKs where an application can use SQL to query raw data coming from Kafka through an API (but that is a topic for another blog). Joining with other datasets.

Kafka 21

Kafka SQL BI Hadoop 21

Cloudera

FEBRUARY 16, 2021

In the previous blog post , we looked at some of the application development concepts for the Cloudera Operational Database (COD). In this blog post, we’ll see how you can use other CDP services with COD. Integrated across the Enterprise Data Lifecycle . Conclusion.

Database 86

Database Machine Learning Kafka Aggregated Data 86

Cloudera

SEPTEMBER 9, 2022

With this in mind, it’s clear that no “one size fits all” architecture will work here; we need a diverse set of data services, fit for each workload and purpose, backed by optimized compute engines and tools. . Data changes in numerous ways: the shape and form of the data changes; the volume, variety, and velocity changes.

Architecture 105

Architecture Metadata Machine Learning Unstructured Data 105

Striim

SEPTEMBER 11, 2024

Data Collection/Ingestion The next component in the data pipeline is the ingestion layer, which is responsible for collecting and bringing data into the pipeline. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) Introduction Ensuring the accuracy and timeliness of data ingestion is a cornerstone for maintaining the integrity of data systems. This process is critical as it ensures data quality from the onset.

Data Ingestion 56

Data Ingestion Transportation High Quality Data Data Schemas 56

Netflix Tech

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” As a result, a single consolidated and centralized source of truth does not exist that can be leveraged to derive data lineage truth. push or pull.

Building 90

Building Metadata Transportation Data Ingestion 90

Rockset

JANUARY 30, 2024

Microbatching : An option to microbatch ingestion based on the latency requirements of the use case. In this blog, we delve into each of these features and how they are giving users more cost controls for their search and AI applications. This is not a hands-free operation and also involves the transfer of data across nodes.

Data Ingestion 64

Data Ingestion Utilities Architecture SQL 64

DataKitchen

NOVEMBER 27, 2024

However, not all data quality dashboards are created equal. Their design and focus vary significantly depending on an organization’s unique goals, challenges, and data landscape. This blog delves into the six distinct types of data quality dashboards, examining how each fulfills a specific role in ensuring data excellence.

Banking 87

Banking Data Datasets Retail 87

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Fast, Safe Development and Deployment (#4) Introduction The integrity and functionality of new code, tools, and configurations during the development and deployment stages are crucial. This process is critical as it ensures data quality from the onset.

Data Ingestion 53

Data Ingestion Datasets Coding Data 53

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. Have I Checked The Raw Data And The Integrated Data?

Raw Data 53

Raw Data Data Ingestion Datasets Data 53

Netflix Tech

OCTOBER 19, 2020

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. An additional implication of a lenient sampling policy is the need for scalable stream processing and storage infrastructure fleets to handle increased data volume. —?which is difficult when troubleshooting distributed systems.

Building 95

Building Transportation Java Metadata 95

Data Engineering Weekly

NOVEMBER 20, 2022

Google AI: The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation Google published Data Cards , a dataset documentation framework aimed at increasing transparency across dataset lifecycles. link] The short YouTube video gives a nice overview of the Data Cards.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Data Engineering Weekly

OCTOBER 30, 2022

I found the blog helpful in understanding the generative model’s historical development and the path forward. link] Sponsored- [New eBook] The Ultimate Data Observability Platform Evaluation Guide Considering investing in a data quality solution? The author explains how to dump the history of blockchains into S3.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Knowledge Hut

APRIL 23, 2024

Two popular approaches that have emerged in recent years are data warehouse and big data. While both deal with large datasets, but when it comes to data warehouse vs big data, they have different focuses and offer distinct advantages. Big data offers several advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Confluent

MAY 22, 2019

Since MQTT is designed for low-power and coin-cell-operated devices, it cannot handle the ingestion of massive datasets. On the other hand, Apache Kafka may deal with high-velocity data ingestion but not M2M. A version of this blog post was originally published on the Scylla blog. Try it yourself.

Kafka 98

Kafka Google Cloud NoSQL Entertainment 98

Data Engineering Weekly

MARCH 5, 2023

The basics of the best practices are to establish Meta’s Ground Truth Maturity Framework [GTMF] [link] Google: Datasets at your fingertips in Google Search Easy access to the datasets is 80% of the problem solved in data engineering. link] The blog highlights six key principles of the value creation of data.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

ProjectPro

FEBRUARY 6, 2023

Data engineers often use Google Cloud Pub/Sub to design asynchronous workflows, publish event notifications, and stream data from several processes or devices. This blog provides an overview of Google Cloud Pub/Sub that will help you understand the framework and its suitable use cases for your data engineering projects.

Google Cloud 52

Google Cloud Cloud Cloud Storage Data Ingestion 52

Confluent

OCTOBER 10, 2019

Apache Kafka ® and its surrounding ecosystem, which includes Kafka Connect, Kafka Streams, and KSQL, have become the technology of choice for integrating and processing these kinds of datasets. MQTT Proxy for data ingestion without an MQTT broker. But that doesn’t move much.

Kafka 20

Kafka Google Cloud Architecture Machine Learning 20

ProjectPro

AUGUST 24, 2021

Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project. And, out of these professions, this blog will discuss the data engineering job role.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Cloudera

SEPTEMBER 7, 2023

Given the complexity of the datasets used to train AI systems, and factoring in the known tendency of generative AI systems to invent non-factual information, this is no small task. By embedding intelligence at the data platform-level, it becomes possible to accelerate the pace at which financial institutions can operationalize AI solutions.

Insurance 86

Insurance Finance Portfolio Government 86

Ascend.io

OCTOBER 28, 2024

Data transformation helps make sense of the chaos, acting as the bridge between unprocessed data and actionable intelligence. You might even think of effective data transformation like a powerful magnet that draws the needle from the stack, leaving the hay behind.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Ascend.io

JUNE 8, 2023

However, transforming data into a product so that it can deliver outsized business value requires more than just a mission statement; it requires a solid foundation of technical capabilities and a truly data-centric culture. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Cloudera

OCTOBER 22, 2020

Replication ( covered in this previous blog article ) has been released for a while and is among the most used features of Apache HBase. Although it is an efficient way of keeping different HBase databases in sync within a sub-second latency, replication only operates over data ingested after the feature has been enabled.

Bytes 65

Bytes Datasets Data Data Ingestion 65

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

AWS 98

AWS Scala Metadata Data Lake 98