Data Science 101

NOVEMBER 11, 2019

Azure Synapse Analytics This is the future of data warehousing. It combines data warehousing and data lakes into a simple query interface for a simple and fast analytics service. AWS Parallel Cluster for Machine Learning AWS Parallel Cluster is an open-source cluster management tool.

Cloud Data 95

Cloud Data Data Science Azure Clustering 95

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 102

Tableau Data Lakes Data Warehouse SQL 102

ODSC - Open Data Science

SEPTEMBER 29, 2023

Data management problems can also lead to data silos; disparate collections of databases that don’t communicate with each other, leading to flawed analysis based on incomplete or incorrect datasets. One way to address this is to implement a data lake: a large and complex database of diverse datasets all stored in their original format.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. It will enable you to quickly transform and load the data results into Amazon S3 data lakes or JDBC data stores.

Apache Kafka 130

Apache Kafka ETL Data Lakes AWS 130

phData

NOVEMBER 9, 2023

A data warehouse is a centralized and structured storage system that enables organizations to efficiently store, manage, and analyze large volumes of data for business intelligence and reporting purposes. What is a Data Lake? What is the Difference Between a Data Lake and a Data Warehouse?

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

Flipboard

NOVEMBER 17, 2023

Set up a MongoDB cluster To create a free tier MongoDB Atlas cluster, follow the instructions in Create a Cluster. Delete the MongoDB Atlas cluster. About the authors Igor Alekseev is a Senior Partner Solution Architect at AWS in Data and Analytics domain. Set up the database access and network access.

K-nearest Neighbors 170

K-nearest Neighbors AWS Clustering Database 170

IBM Journey to AI blog

SEPTEMBER 25, 2023

But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Every day, millions of riders use the Uber app, unwittingly contributing to a complex web of data-driven decisions. Consider the magnitude of Uber’s footprint.

Data Lakes 102

Data Lakes Analytics Analytics Clustering 102

phData

NOVEMBER 8, 2024

The most used open table formats currently are Apache Iceberg, Delta Lake, and Apache Hudi. These systems are built on open standards and offer immense analytical and transactional processing flexibility. Adopting an Open Table Format architecture is becoming indispensable for modern data systems. Why are They Essential?

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

Smart Data Collective

FEBRUARY 20, 2022

Whether it’s data management, analytics, or scalability, AWS can be the top-notch solution for any SaaS company. Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps.

AWS 138

AWS Cloud Computing Data Lakes Database 138

AWS Machine Learning Blog

FEBRUARY 21, 2025

Thats why we use advanced technology and data analytics to streamline every step of the homeownership experience, from application to closing. This also led to a backlog of data that needed to be ingested. This created a challenge for data scientists to become productive. Analytic data is stored in Amazon Redshift.

Data Science 84

Data Science AWS Hadoop Data Scientist 84

ODSC - Open Data Science

APRIL 24, 2023

Botnet Detection at Scale — Lessons Learned From Clustering Billions of Web Attacks Into Botnets Editor’s note: Ori Nakar is a speaker for ODSC Europe this June. Be sure to check out his talk, “ Botnet detection at scale — Lesson learned from clustering billions of web attacks into botnets ,” there!

Clustering 52

Clustering SQL Algorithm Data Science 52

AWS Machine Learning Blog

DECEMBER 18, 2023

Among all, the native time series capabilities is a standout feature, making it ideal for a managing high volume of time-series data, such as business critical application data, telemetry, server logs and more. With efficient querying, aggregation, and analytics, businesses can extract valuable insights from time-stamped data.

Clustering 123

Clustering AWS Database ML 123

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster.

ML 123

ML ML AWS Data Warehouse 123

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

Women in Big Data

NOVEMBER 27, 2024

It consolidates data from various systems, such as transactional databases, CRM platforms, and external data sources, enabling organizations to perform complex queries and derive insights. By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Pickl AI

DECEMBER 2, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

AWS Machine Learning Blog

JUNE 21, 2024

eSentire has over 2 TB of signal data stored in their Amazon Simple Storage Service (Amazon S3) data lake. This further step updates the FM by training with data labeled by security experts (such as Q&A pairs and investigation conclusions). They needed no additional infrastructure for data integration.

AWS 128

AWS AI AI Natural Language Processing 128

phData

SEPTEMBER 20, 2023

phData has been working in data engineering since the inception of the company back in 2015. We have seen customers transform their data analytics with Snowflake and transform their data engineering and machine learning applications with Spark, Java, Scala, and Python.

SQL 98

SQL Python Data Lakes Machine Learning 98

AWS Machine Learning Blog

AUGUST 15, 2024

You need data engineering expertise and time to develop the proper scripts and pipelines to wrangle, clean, and transform data. Afterward, you need to manage complex clusters to process and train your ML models over these large-scale datasets. Explore the future of no-code ML with SageMaker Canvas today.

ML 127

ML ML Data Preparation AWS 127

AWS Machine Learning Blog

MAY 15, 2023

Algorithm Selection Amazon Forecast has six built-in algorithms ( ARIMA , ETS , NPTS , Prophet , DeepAR+ , CNN-QR ), which are clustered into two groups: statististical and deep/neural network. He joined Getir in 2019 and currently works as a Senior Data Science & Analytics Manager.

Algorithm 100

Algorithm Data Scientist Machine Learning Machine Learning 100

Pickl AI

FEBRUARY 21, 2023

It involves using statistical and computational techniques to identify patterns and trends in the data that are not readily apparent. Data mining is often used in conjunction with other data analytics techniques, such as machine learning and predictive analytics, to build models that can be used to make predictions and inform decision-making.

Data Mining 52

Data Mining Data Mining Data Mining Data Scientist 52

Pickl AI

SEPTEMBER 17, 2024

The importance of Big Data lies in its potential to provide insights that can drive business decisions, enhance customer experiences, and optimise operations. Organisations can harness Big Data Analytics to identify trends, predict outcomes, and make informed decisions that were previously unattainable with smaller datasets.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JANUARY 28, 2025

Research indicates that companies utilizing advanced analytics are 5 times more likely to make faster decisions than their competitors. Key Components of Business Intelligence Architecture Business Intelligence (BI) architecture is a structured framework that enables organizations to gather, analyze, and present data effectively.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

DagsHub

NOVEMBER 11, 2024

Video : Movies, live streams, and CCTV footage combine visual and audio data, making them highly complex. Video analytics enable object detection, motion tracking, and behavioural analysis for security, traffic monitoring, or customer engagement insights. This will ensure the data is in an ideal structure for further analysis.

AI 52

AI AI Data Lakes Database 52

Pickl AI

JULY 30, 2024

Flexibility : NiFi supports a wide range of data sources and formats, allowing organizations to integrate diverse systems and applications seamlessly. Scalability : NiFi can be deployed in a clustered environment, enabling organizations to scale their data processing capabilities as their data needs grow.

ETL 52

ETL Data Lakes Big Data Big Data 52

Alation

JULY 18, 2022

Tell them to grab a catalog … and go jump in a lake. That was the message — delivered a little more elegantly than that — at Databricks’ Data+AI Summit 2022. Automatically tracking data lineage across queries executed in any language. Destination Lakehouse. The theme of the summit was Destination Lakehouse. and much more!

AI 52

AI AI Data Lakes Azure 52

phData

APRIL 2, 2024

By leveraging cloud-based data platforms such as Snowflake Data Cloud , these commercial banks can aggregate and curate their data to understand individual customer preferences and offer relevant and personalized products. so that organizations can focus on delivering value rather than be burdened by operational complexities.

ML 52

ML ML Data Silos Data Lakes 52

phData

DECEMBER 4, 2023

Thirty seconds is a good default for human users; if you find that queries are regularly queueing, consider making your warehouse a multi-cluster that scales on-demand. Cluster Count If your warehouse has to serve many concurrent requests, you may need to increase the cluster count to meet demand.

Clustering 98

Clustering Database SQL Data Pipeline 98

Pickl AI

JULY 6, 2023

It acts as a catalogue, providing information about the structure and location of the data. · Hive Query Processor It translates the HiveQL queries into a series of MapReduce jobs. · Hive Execution Engine It executes the generated query plans on the Hadoop cluster. It manages the execution of tasks across different environments.

Hadoop 52

Hadoop SQL Big Data Big Data 52

ODSC - Open Data Science

JULY 11, 2023

It uses a form of artificial intelligence called Reinforcement Learning from Human Feedback to produce answers based on human-guided computer analytics.2 Then I asked about the build or buy options to finance data centers or alternatives; this is covered in Part 2 as well. Its response is below. Not a cloud computer?

Data Lakes 40

Data Lakes AI AI Cloud Computing 40

Pickl AI

NOVEMBER 4, 2024

Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. It involves developing data pipelines that efficiently transport data from various sources to storage solutions and analytical tools. ETL is vital for ensuring data quality and integrity.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Snorkel AI

MARCH 26, 2024

Snorkel Flow’s programmatic labeling process starts with labeling functions—essentially programmable rules to label data. Snorkel Flow users can build labeling functions according to various data features—from continuous variable thresholds to vector embedding clusters. Our client completed this task in a couple of hours.

Machine Learning 52

Machine Learning Machine Learning Data Lakes Data Science 52

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Snorkel AI

MARCH 26, 2024

Snorkel Flow’s programmatic labeling process starts with labeling functions—essentially programmable rules to label data. Snorkel Flow users can build labeling functions according to various data features—from continuous variable thresholds to vector embedding clusters. Our client completed this task in a couple of hours.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52