Accessibility, Aggregated Data, Data Ingestion and Events

Accessibility

Aggregated Data

Data Ingestion

Events

How Snowflake Enhanced GTM Efficiency with Data Sharing and Outreach Customer Engagement Data

Snowflake

APRIL 9, 2024

However, that data must be ingested into our Snowflake instance before it can be used to measure engagement or help SDR managers coach their reps — and the existing ingestion process had some pain points when it came to data transformation and API calls. Each of these sources may store data differently.

BI Data Ingestion Data Aggregated Data

Druid Deprecation and ClickHouse Adoption at Lyft

Lyft Engineering

NOVEMBER 29, 2023

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. Druid enables low latency (real-time) data ingestion, flexible data exploration and fast data aggregation resulting in sub-second query latencies.

Kafka

Kafka Data Ingestion Datasets Architecture

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How to Optimize the Developer Experience for Monumental Impact

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Leading the Development of Profitable and Sustainable Products

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Trending Sources

Tips to Build a Robust Data Lake Infrastructure

DareData

JULY 5, 2023

The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format. Typically, it is advisable to retain the data in its original, unaltered format when transferring it from any source to the data lake layer.

Data Lake

Data Lake Building Raw Data ETL Tools

Webinars

How to Optimize the Developer Experience for Monumental Impact

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Leading the Development of Profitable and Sustainable Products

MORE WEBINARS

How Rockset Enables SQL-Based Rollups for Streaming Data

Rockset

AUGUST 30, 2021

It eliminates the cost and complexity around data preparation, performance tuning and operations, helping to accelerate the movement from batch to real-time analytics. The latest Rockset release, SQL-based rollups, has made real-time analytics on streaming data a lot more affordable and accessible.

SQL

SQL Kafka MongoDB MySQL

Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

FEBRUARY 6, 2019

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. integration) and preprocessing need to run at scale.

Machine Learning

Machine Learning Python Kafka Java

Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

DECEMBER 7, 2021

The second step for building etl pipelines is data transformation, which entails converting the raw data into the format required by the end-application. The transformed data is then placed into the destination data warehouse or data lake. It can also be made accessible as an API and distributed to stakeholders.

Data Pipeline

Data Pipeline Architecture Kafka AWS

Comparing ClickHouse vs Rockset for Event and CDC Streams

Rockset

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. Event streams, such as clickstreams, IoT data and other time series data, are common sources of data into these apps. ClickHouse has several storage engines that can pre-aggregate data.

MySQL

MySQL Kafka Aggregated Data Architecture

20+ Data Engineering Projects for Beginners with Source Code

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering

Data Engineering Data Engineer Coding Project

AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

FEBRUARY 8, 2023

Application programming interfaces (APIs) are used to modify the retrieved data set for integration and to support users in keeping track of all the jobs. Users can schedule ETL jobs, and they can also choose the events that will trigger them. Create schedules or events that will act as job triggers.

AWS

AWS Scala Metadata Data Lake

A Breakthrough Architecture for Real-Time Analytics- An Overview of Compute-Compute Separation in Rockset

Rockset

MARCH 1, 2023

Developers can spin up or down virtual instances based on the performance requirements of their streaming ingest or query workloads. In addition, Rockset provides fast data access through the use of more performant hot storage, while cloud storage is used for durability.

Architecture

Architecture AWS SQL Cloud Storage

A Beginner’s Guide to Learning PySpark for Big Data Processing

ProjectPro

JANUARY 25, 2022

When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems. To access the configuration value, use get(key, defaultValue=None). Distributed - The data in a cluster is distributed among the various nodes.

Big Data

Big Data Data Process Process Kafka

Sqoop vs. Flume Battle of the Hadoop ETL tools

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment. then you are on the right page.

ETL Tools

ETL Tools Hadoop Relational Database Unstructured Data

5 Steps for Migrating from Elasticsearch to Rockset for Real-Time Analytics

Rockset

NOVEMBER 1, 2022

Step 1: Data Acquisition Elasticsearch is rarely the system of record which means the data in it comes from somewhere else for real-time analytics. Rockset has built-in connectors to stream real-time data for testing and simulating production workloads including Apache Kafka , Kinesis and Event Hubs.

Database-centric

Database-centric Pipeline-centric SQL Aggregated Data

The Good and the Bad of the Elasticsearch Search and Analytics Engine

AltexSoft

SEPTEMBER 21, 2023

Accessible via a unified API, these new features enhance search relevance and are available on Elastic Cloud. The Elastic Stacks Elasticsearch is integral within analytics stacks, collaborating seamlessly with other tools developed by Elastic to manage the entire data workflow — from ingestion to visualization.

Engineering

Engineering NoSQL Programming Language Java

What is Data Engineering? Everything You Need to Know in 2022

phData: Data Engineering

JANUARY 3, 2022

The big data analytics market is set to reach $103 billion by 2023 , with poor data quality costing the US economy up to $3.1 Fortune 1000 companies can gain more than $65 million additional net income, only by increasing their data accessibility by 10%. What is Data Governance? trillion yearly.

Data Engineering

Data Engineering Data Engineer Engineering Data Governance

20 Best Open Source Big Data Projects to Contribute on GitHub

ProjectPro

NOVEMBER 15, 2021

When any particular project is open-sourced, it makes the source code accessible to anyone. The adaptability and technical superiority of such open-source big data projects make them stand out for community use. It serves as a distributed processing engine for both categories of data streams: unbounded and bounded.

Big Data

Big Data Project Metadata Programming Language

Handling Out-of-Order Data in Real-Time Analytics Applications

Rockset

APRIL 15, 2022

It’s probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time. It’s probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time. Transmitting out-of-order data is not the issue.

Analytics Application

Analytics Application Data Warehouse Raw Data Kafka

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

AltexSoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization. Event streams.

IT Data Warehouse Data Governance Data Lake

Data Engineering Digest

How Snowflake Enhanced GTM Efficiency with Data Sharing and Outreach Customer Engagement Data

Druid Deprecation and ClickHouse Adoption at Lyft

Webinars

Trending Sources

Tips to Build a Robust Data Lake Infrastructure

Webinars

How Rockset Enables SQL-Based Rollups for Streaming Data

Machine Learning with Python, Jupyter, KSQL and TensorFlow

Data Pipeline- Definition, Architecture, Examples, and Use Cases

Comparing ClickHouse vs Rockset for Event and CDC Streams

20+ Data Engineering Projects for Beginners with Source Code

AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

A Breakthrough Architecture for Real-Time Analytics- An Overview of Compute-Compute Separation in Rockset

A Beginner’s Guide to Learning PySpark for Big Data Processing

Sqoop vs. Flume Battle of the Hadoop ETL tools

5 Steps for Migrating from Elasticsearch to Rockset for Real-Time Analytics

The Good and the Bad of the Elasticsearch Search and Analytics Engine

What is Data Engineering? Everything You Need to Know in 2022

20 Best Open Source Big Data Projects to Contribute on GitHub

Handling Out-of-Order Data in Real-Time Analytics Applications

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

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