BizAcuity

APRIL 1, 2023

Techniques to large data handling Power BI uses import models that are loaded with data, which is then compressed and optimized and then stored to disk. When source data is loaded into memory, it is possible to see 10x compression, and so it is reasonable to expect that 10 GB of source data can compress to about 1 GB in size.

Optimization 52

Optimization Dashboards Modeling Sales 52

Cloudera

JULY 21, 2022

Cloudera Data Warehouse). Efficient batch data processing. Complex data transformations. Support for data rollup and summarization. Highly optimized time series queries. This reduces the total cost of ownership and frees internal resources for higher priority tasks than Druid maintenance and optimization.

Metrics 83

Metrics Slice and Dice Data Warehouse Dashboards 83

Cloudera

OCTOBER 13, 2020

Data Warehouse – in addition to a number of performance optimizations, DW has added a number of new features for better scalability, monitoring and reliability to enable self-service access with security and performance . Predict – Data Engineering (Apache Spark). New Services. Learn More, Keep in Touch.

Data Warehouse 92

Data Warehouse Machine Learning Visualization Data Lake 92

BizAcuity

SEPTEMBER 21, 2022

Power BI uses import models that are loaded with data, which is then compressed and optimized and then stored to disk. When source data is loaded into memory, it is possible to see 10x compression, and so it is reasonable to expect that 10 GB of source data can compress to about 1 GB in size.

Optimization 52

Optimization Dashboards Modeling Sales 52

Ontotext

MARCH 20, 2024

The Bridge to Unified Data and Growth Imagine a world where your sales and marketing teams can effortlessly access and utilize data from various sources – LinkedIn, ZoomInfo, DBpedia, Yahoo Finance, and even your internal data sources – all within the familiar interface of Salesforce.

Data-driven 52

Data-driven Strategy Sales Data Integration 52

CIO Business Intelligence

JULY 5, 2022

What is the difference between business analytics and data analytics? Business analytics is a subset of data analytics. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 123

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 123

AWS Big Data

APRIL 2, 2024

With these settings, you can now seamlessly ingest decompressed CloudWatch log data into Splunk using Firehose. This enables you to run high-performance, cost-efficient analytics on streaming data in Amazon S3 using services such as Amazon Athena , Amazon EMR , Amazon Redshift Spectrum , and Amazon QuickSight.

Metadata 84

Metadata Marketing Analytics Data Transformation 84

AWS Big Data

MAY 25, 2023

Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset.

Reporting 67

Reporting Metrics Optimization Data Lake 67

IBM Big Data Hub

JULY 28, 2023

Since its launch in 2020, DATA ONE has been successfully adopted by multinational companies across sectors, including insurance and banking, automotive, energy and utilities, manufacturing, logistics and telco. DATA ONE consists of three modules that can be activated as needed: Data Mover , a secure file-transfer enterprise solution.

Data Integration 60

Data Integration Optimization Software Insurance 60

CIO Business Intelligence

JANUARY 24, 2024

At Vanguard, “data and analytics enable us to fulfill on our mission to provide investors with the best chance for investment success by enabling us to glean actionable insights to drive personalized client experiences, scale advice, optimize investment and business operations, and reduce risk,” Swann says.

Data Strategy 133

Data Strategy Strategy Unstructured Data Data Governance 133

bridgei2i

NOVEMBER 17, 2020

The Lean AI wave can be imagined as a 4 step process: AI use case discovery: Identify the current processes amenable to data and AI driven improvement, design the solution roadmap and proactively think through the potential failure modes of enterprise adoption.

Deep Learning 85

Deep Learning Manufacturing Optimization Data-driven 85

Cloudera

DECEMBER 21, 2021

Since the release of Cloudera Data Engineering (CDE) more than a year ago , our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. This enabled new use-cases with customers that were using a mix of Spark and Hive to perform data transformations. .

Snapshot 115

Snapshot Data-driven Optimization Management 115

Sisense

OCTOBER 15, 2020

Let’s look at a few ways that different industries take advantage of streaming data. How industries can benefit from streaming data. Automotive: Monitoring connected, autonomous cars in real time to optimize routes to avoid traffic and for diagnosis of mechanical issues. Optimizing object storage.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

datapine

SEPTEMBER 29, 2022

This means there are no unintended data errors, and it corresponds to its appropriate designation (e.g., Here, it all comes down to the data transformation error rate. In other words, it measures the time between when data is expected and the moment when it is readily available for use. date, month, and year).

Data Quality 241

Data Quality Metrics Data-driven Management 241

AWS Big Data

FEBRUARY 6, 2023

When migrating Hadoop workloads to Amazon EMR , it’s often difficult to identify the optimal cluster configuration without analyzing existing workloads by hand. It enables compute such as EMR instances and storage such as Amazon Simple Storage Service (Amazon S3) data lakes to scale. For more information, see the GitHub repo.

Cost-Benefit 52

Cost-Benefit Data Lake Dashboards Big Data 52

AWS Big Data

JUNE 6, 2023

Furthermore, it allows for necessary actions to be taken, such as rectifying errors in the data source, refining data transformation processes, and updating data quality rules. This significantly enhances the accuracy and reliability of your data. Select your stack and delete it.

Data Quality 72

Data Quality Metrics Data-driven Visualization 72

AWS Big Data

JANUARY 27, 2023

Kinesis Data Firehose uses Lambda to perform data transformation and compression, storing the file in a compressed columnar format (Parquet) in the target S3 bucket. The AWS Glue Data Catalog has the table definitions for the data sources. To optimize query performance, we use Athena partition projections.

Analytics 60

Analytics Reporting Dashboards Visualization 60

AWS Big Data

MARCH 13, 2024

This method uses GZIP compression to optimize storage consumption and query performance. You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches.

Analytics 94

Analytics IoT Metadata Internet of Things 94

AWS Big Data

JANUARY 17, 2024

The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query. By identifying these changes, the query engine can optimize the query to process only the relevant data, significantly reducing the processing time and resource requirements.

Data Lake 92

Data Lake Snapshot Big Data Data-driven 92

IBM Big Data Hub

OCTOBER 16, 2023

It also lets you choose the right engine for the right workload at the right cost, potentially reducing your data warehouse costs by optimizing workloads. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

Risk 77

Risk Modeling Management Metadata 77

AWS Big Data

FEBRUARY 26, 2024

When you start the process of designing your data model for Amazon Keyspaces, it’s essential to possess a comprehensive understanding of your access patterns, similar to the approach used in other NoSQL databases. Additionally, you can configure OpenSearch Ingestion to apply data transformations before delivery.

Dashboards 62

Dashboards Testing Metrics Optimization 62

Insight

APRIL 8, 2020

Having run a data engineering program at Insight for several years, we’ve identified three broad categories of data engineers: Software engineers who focus on building data pipelines. In some cases, they work to deploy data science models into production with an eye towards optimization, scalability and maintainability.

Cost-Benefit 96

Cost-Benefit Software Data Warehouse Optimization 96

Sisense

MARCH 14, 2021

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

AWS Big Data

OCTOBER 12, 2023

With EMR Serverless, you don’t have to configure, optimize, secure, or operate clusters to run applications with these frameworks. You can run analytics workloads at any scale with automatic scaling that resizes resources in seconds to meet changing data volumes and processing requirements.

Big Data 105

Big Data Data-driven Management Visualization 105

CDW Research Hub

JUNE 14, 2019

Channel partners are critical to helping customers achieve data transformation, and today we recognize and celebrate their accomplishments,” said Jeff McCullough, vice president of Channel Sales for NetApp. “I “Sirius works hard to provide technology that not only supports our clients’ business outcomes, but delivers on innovation.

Sales 40

Sales Marketing Data Transformation Optimization 40

AWS Big Data

MARCH 9, 2023

Additionally, there are major rewrites to deliver developer-focused improvements, including static type checking, enhanced runtime validation, strong consistency in call patterns, and optimized event chaining.

Slice and Dice 77

Slice and Dice Dashboards Analytics Interactive 77

Sisense

FEBRUARY 26, 2020

Achieving a successful data transformation requires the right choice of an analytics and BI platform to ensure you get the most value from your data.

Digital Transformation 90

Digital Transformation Analytics Cost-Benefit Data-driven 90

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. You can use it for big data analytics and machine learning workloads.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

Cloudera

MARCH 14, 2023

A critical feature for every developer however is to get instantaneous feedback like configuration validations or performance metrics, as well as previewing data transformations for each step of their data flow. You can create DataFlow Deployments using the Deployment Wizard, or automate them using the CDP CLI.

Testing 81

Testing Publishing Metadata Interactive 81

Cloudera

JUNE 1, 2021

Our customers must also have secure access to their data from anywhere – from on-premises to hybrid clouds and multiple public clouds. We must integrate and optimize the end-to-end data lifecycle for our customers, empowering them to focus on what really matters – extracting value from their data.

Uncertainty 144

Uncertainty Cost-Benefit Risk Strategy 144

AWS Big Data

NOVEMBER 15, 2023

BMW Group uses 4,500 AWS Cloud accounts across the entire organization but is faced with the challenge of reducing unnecessary costs, optimizing spend, and having a central place to monitor costs. The ultimate goal is to raise awareness of cloud efficiency and optimize cloud utilization in a cost-effective and sustainable manner.

Dashboards 94

Dashboards Analytics Metadata Data Warehouse 94

AWS Big Data

MAY 16, 2023

Data collection and processing are handled by a third-party smart sensor manufacturer application residing in Amazon Virtual Private Cloud (Amazon VPC) private subnets behind a Network Load Balancer. The AWS Glue Data Catalog contains the table definitions for the smart sensor data sources stored in the S3 buckets.

Dashboards 75

Dashboards Statistics Data Collection Business Intelligence 75

Alation

FEBRUARY 2, 2023

Having the right tools is essential for any successful data product manager focused on enterprise data transformation. When choosing the tools for a project, whether it be the CIO , CDO , or data product managers themselves, the buyers must see the big picture. They can quickly understand complex systems and technology.

Management 52

Management Data-driven Visualization Strategy 52

CDW Research Hub

JANUARY 22, 2020

From evaluation and design to implementation, financing and managed services, Sirius can help you develop a successful cloud adoption strategy to optimize application and service delivery, while safely migrating, managing and running applications and workloads.

Measurement 52

Measurement Consulting Risk Finance 52

DataRobot Blog

MARCH 10, 2022

The first step in building a model that can predict machine failure and even recommend the next best course of action is to aggregate, clean, and prepare data to train against. This task may require complex joins, aggregations, filtering, window functions, and many other data transformations against extremely large-scale data sets.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

AWS Big Data

JULY 31, 2023

This concludes creating data sources on the AWS Glue job canvas. Next, we add transformations by combining data from these different tables. Transform the data Complete the following steps to add data transformations: On the AWS Glue job canvas, choose the plus sign. Wait for all the jobs to complete.

Sales 52

Sales Data Warehouse Visualization Testing 52

Cloudera

AUGUST 17, 2021

When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. Typically users need to ingest data, transform it into optimal format with quality checks, and optimize querying of the data by visual analytics tool.

Management 111

Management Cost-Benefit Data Transformation Optimization 111