Hepta Analytics

FEBRUARY 14, 2022

DE Zoomcamp 2.2.1 – Introduction to Workflow Orchestration Following last weeks blog , we move to data ingestion. We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Rockset

MARCH 27, 2019

The application you're implementing needs to analyze this data, combining it with other datasets, to return live metrics and recommended actions. But how can you interrogate the data and frame your questions correctly if you don't understand the shape of your data? This enables Rockset to generate a Smart Schema on the data.

Raw Data 40

Raw Data SQL NoSQL Datasets 40

Cloudera

JANUARY 20, 2021

The data journey is not linear, but it is an infinite loop data lifecycle – initiating at the edge, weaving through a data platform, and resulting in business imperative insights applied to real business-critical problems that result in new data-led initiatives. Data Collection Using Cloudera Data Platform.

Manufacturing 110

Manufacturing Data Warehouse Kafka Retail 110

Cloudera

MAY 19, 2021

Data Ingestion. The raw data is in a series of CSV files. We will firstly convert this to parquet format as most data lakes exist as object stores full of parquet files. Common problems at this stage can be related to GPU versions. RAPIDS is only supported on Pascal or newer NVIDIA GPUs.

Machine Learning 137

Machine Learning Data Science Datasets Raw Data 137

Towards Data Science

NOVEMBER 16, 2023

Data Management A tutorial on how to use VDK to perform batch data processing Photo by Mika Baumeister on Unsplash Versatile Data Ki t (VDK) is an open-source data ingestion and processing framework designed to simplify data management complexities. link] Summary Congratulations!

Data Process 98

Data Process Process Raw Data Data 98

Cloudera

NOVEMBER 17, 2020

The data and the techniques presented in this prototype are still applicable as creating a PCA feature store is often part of the machine learning process. . The process followed in this prototype covers several steps that you should follow: Data Ingest – move the raw data to a more suitable storage location.

Deep Learning 115

Deep Learning Machine Learning Raw Data Data Ingestion 115

Striim

SEPTEMBER 11, 2024

These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed. 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.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Snowflake

JUNE 6, 2024

Schedule data ingestion, processing, model training and insight generation to enhance efficiency and consistency in your data processes. Access Snowflake platform capabilities and data sets directly within your notebooks.

SQL 115

SQL Python Machine Learning Data Workflow 115

Cloudera

NOVEMBER 19, 2020

The modeling process begins with data collection. Here, Cloudera Data Flow is leveraged to build a streaming pipeline which enables the collection, movement, curation, and augmentation of raw data feeds. These feeds are then enriched using external data sources (e.g.,

Government 119

Government Machine Learning Algorithm Raw Data 119

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 105

Engineering Raw Data Data Science Machine Learning 105

Data Engineering Podcast

JUNE 26, 2022

Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. In fact, while only 3.5% That’s where our friends at Ascend.io

Data Management 100

Data Management Management MongoDB MySQL 100

Towards Data Science

MAY 3, 2023

One such tool is the Versatile Data Kit (VDK), which offers a comprehensive solution for controlling your data versioning needs. VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python.

Data Lake 98

Data Lake SQL Data Data Warehouse 98

Ascend.io

FEBRUARY 6, 2023

Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products. Data Plane – is the data cloud where the data pipeline workload runs, like Databricks, BigQuery, and Snowflake.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Ascend.io

FEBRUARY 6, 2023

Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products. Data Plane – is the data cloud where the data pipeline workload runs, like Databricks, BigQuery, and Snowflake.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Ascend.io

AUGUST 15, 2023

While terms like “Fivetran ETL” or “Fivetran data pipeline” are echoing in the corridors of data professionals, the truth is, Fivetran is primarily an expert on data ingestion — just the first step in a much broader and nuanced data management process.

Data Management 52

Data Management Management Data Ingestion Data Pipeline 52

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). However, Apache Kafka is more than just messaging.

Kafka 21

Kafka SQL BI Hadoop 21

DataKitchen

MAY 10, 2024

The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset. Examples include regular loading of CRM data and anomaly detection.

Raw Data 49

Raw Data Data Ingestion Datasets Data 49

Monte Carlo

APRIL 24, 2023

In this article, we’ll dive deep into the data presentation layers of the data stack to consider how scale impacts our build versus buy decisions, and how we can thoughtfully apply our five considerations at various points in our platform’s maturity to find the right mix of components for our organizations unique business needs.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Knowledge Hut

JANUARY 18, 2024

Let us now look into the differences between AI and Data Science: Data Science vs Artificial Intelligence [Comparison Table] SI Parameters Data Science Artificial Intelligence 1 Basics Involves processes such as data ingestion, analysis, visualization, and communication of insights derived.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Snowflake

SEPTEMBER 11, 2023

The intention of Dynamic Tables is to apply incremental transformations on near real-time data ingestion that Snowflake now supports with Snowpipe Streaming. Data enters Snowflake in its raw operational form (event data) and Dynamic Tables transforms that raw data into a form that serves analytical value.

SQL 102

SQL Data Raw Data Architecture 102

Cloudyard

MARCH 4, 2023

DCDW Architecture Above all, Architecture was divided into three Business layers: Firstly,Agile Data ingestion : Heterogeneous Source System fed the data into Cloud. Respective Cloud would consume/Store the data in bucket or containers. Load the data AS-IS into Snowflake called RAW layer.

Architecture 52

Architecture Cloud Metadata Data Ingestion 52

DataKitchen

SEPTEMBER 21, 2023

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. The current landscape of Data Observability Tools shows a marked focus on “Data in Place,” leaving a significant gap in the “Data in Use.”

Raw Data 67

Raw Data Data Business Intelligence Data Engineer 67

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Video explaining how data streaming works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Snowflake

MARCH 13, 2023

Architecture designed to empower more clients Gem’s cybersecurity platform starts with raw data ingestion from its clients’ cloud environments. Gem uses the fully-managed Snowpipe service, allowing it to stream and process source data in near-real time. Pushing and scaling are super smooth.

Cloud 52

Cloud Building Data Ingestion AWS 52

Knowledge Hut

DECEMBER 7, 2023

Welcome to the comprehensive guide for beginners on harnessing the power of Microsoft's remarkable data visualization tool - Power BI. In today's data-driven world, the ability to transform raw data into meaningful insights is paramount, and Power BI empowers users to achieve just that. What is Power BI?

BI 52

BI Raw Data Datasets Business Intelligence 52

Ascend.io

AUGUST 29, 2023

Similarly , in data, every step of the pipeline, from data ingestion to delivery, plays a pivotal role in delivering impactful results. In this article, we’ll break down the intricacies of an end-to-end data pipeline and highlight its importance in today’s landscape.

Data Pipeline 52

Data Pipeline Pipeline-centric Database-centric Data Ingestion 52

DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Ascend.io

JANUARY 2, 2024

The key differentiation lies in the transformational steps that a data pipeline includes to make data business-ready. Ultimately, the core function of a pipeline is to take raw data and turn it into valuable, accessible insights that drive business growth. cleaning, formatting)?

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Monte Carlo

SEPTEMBER 25, 2024

A data lake is essentially a vast digital dumping ground where companies toss all their raw data, structured or not. A modern data stack can be built on top of this data storage and processing layer, or a data lakehouse or data warehouse, to store data and process it before it is later transformed and sent off for analysis.

Data Lake 52

Data Lake Food Google Cloud AWS 52

Zalando Engineering

MARCH 21, 2017

Instead, we can focus on building a flexible and versatile model that can be easily extended to new types of input data and applied to a variety of prediction tasks. In general, learning from raw data can help to avoid limitations when placing too much confidence in human domain modeling.

Deep Learning 52

Deep Learning Raw Data Machine Learning AWS 52

The Modern Data Company

MAY 8, 2023

The Data Lake: A Reservoir of Unstructured Potential A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

The Data Lake: A Reservoir of Unstructured Potential A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

The Data Lake: A Reservoir of Unstructured Potential A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs.

Data Management 52

Data Management Management Data Lake Data Governance 52

Ascend.io

FEBRUARY 23, 2024

For this reason, your data platform becomes the foundation for your AI initiatives. Robust Data Ingestion AI systems thrive on diverse data sources. Your platform should be equipped with robust mechanisms for data ingestion and integration, enabling seamless flow of data from various sources into the system.

Cloud Storage 52

Cloud Storage Data Ingestion Machine Learning Algorithm 52

Snowflake

MARCH 27, 2023

It seems everyone has a handful of such shapes in their raw data, and in the past they had to fix those shapes outside of Snowflake before ingesting them. Introducing CARTO Workflows Snowflake’s powerful data ingestion and transformation features help many data engineers and analysts who prefer SQL.

Generalist 52

Generalist Raw Data Business Analyst SQL 52

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses.

AWS 98

AWS Scala Metadata Data Lake 98