AWS Big Data

JANUARY 17, 2024

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. 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.

Data Lake 92

Data Lake Snapshot Big Data Data-driven 92

AWS Big Data

MARCH 9, 2023

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Slice and Dice 77

Slice and Dice Dashboards Analytics Interactive 77

AWS Big Data

APRIL 19, 2023

We took this a step further by creating a blueprint to create smart recommendations by linking similar data products using graph technology and ML. In this post, we showed how an organization can augment a data catalog with additional metadata by using ML and Neptune with an automated process.

Technology 80

Technology Data-driven Machine Learning Sales 80

AWS Big Data

APRIL 25, 2023

The following AWS services are used for data ingestion, processing, and load: Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications like Salesforce, SAP, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift , in just a few clicks.

Sales 69

Sales Visualization Software Marketing 69

AWS Big Data

OCTOBER 11, 2023

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

Management 104

Management Metadata Analytics Dashboards 104

AWS Big Data

NOVEMBER 15, 2023

It seamlessly consolidates data from various data sources within AWS, including AWS Cost Explorer (and forecasting with Cost Explorer ), AWS Trusted Advisor , and AWS Compute Optimizer. Data providers and consumers are the two fundamental users of a CDH dataset. You might notice that this differs slightly from traditional ETL.

Dashboards 94

Dashboards Analytics Metadata Data Warehouse 94

AWS Big Data

APRIL 12, 2023

Amazon EMR on EKS provides a deployment option for Amazon EMR that allows organizations to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). About the Authors Melody Yang is a Senior Big Data Solution Architect for Amazon EMR at AWS. As of the Amazon EMR 6.5 with up to 61% lower costs.

Testing 73

Testing Big Data Metadata Optimization 73

Alation

JANUARY 17, 2023

Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? Reverse ETL tools.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

IBM Big Data Hub

JANUARY 9, 2024

The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. 4 key components to ensure reliable data ingestion Data quality and governance: Data quality means ensuring the security of data sources, maintaining holistic data and providing clear metadata.

Enterprise 81

Enterprise Data Integration Data Quality Contextual Data 81

AWS Big Data

OCTOBER 18, 2023

To run HiveQL-based data workloads with Spark on Kubernetes mode, engineers must embed their SQL queries into programmatic code such as PySpark, which requires additional effort to manually change code. About the authors Amit Maindola is a Senior Data Architect focused on big data and analytics at Amazon Web Services.

Big Data 79

Big Data Data Processing Interactive Testing 79

AWS Big Data

JULY 20, 2023

The Orca Platform is powered by a state-of-the-art anomaly detection system that uses cutting-edge ML algorithms and big data capabilities to detect potential security threats and alert customers in real time, ensuring maximum security for their cloud environment. This ensures that the data is suitable for training purposes.

Data Lake 85

Data Lake Analytics Snapshot Optimization 85

IBM Big Data Hub

JANUARY 18, 2023

This adds an additional ETL step, making the data even more stale. Data lakehouse was created to solve these problems. The data warehouse storage layer is removed from lakehouse architectures. Instead, continuous data transformation is performed within the BLOB storage. Data fabric promotes data discoverability.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

IBM Big Data Hub

MARCH 14, 2024

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging.

Cost-Benefit 84

Cost-Benefit Metadata Optimization Management 84

AWS Big Data

OCTOBER 11, 2023

Due to this low complexity, the solution uses AWS serverless services to ingest the data, transform it, and make it available for analytics. The Data Catalog now contains references to the machine-readable data. Use the Data Catalog and transform the hospital price transparency data.

Visualization 96

Visualization Dashboards Data-driven Gap analysis 96

AWS Big Data

NOVEMBER 16, 2023

Data Vault 2.0 allows for the following: Agile data warehouse development Parallel data ingestion A scalable approach to handle multiple data sources even on the same entity A high level of automation Historization Full lineage support However, Data Vault 2.0

Enterprise 83

Enterprise Data Warehouse Data Lake Optimization 83

IBM Big Data Hub

JULY 17, 2023

foundation models to help users discover, augment, and enrich data with natural language. Watsonx.data is built on 3 core integrated components: multiple query engines, a catalog that keeps track of metadata, and storage and relational data sources which the query engines directly access.

Machine Learning 68

Machine Learning Data Warehouse Modeling Cost-Benefit 68

AWS Big Data

FEBRUARY 6, 2023

The task_id used can be the name of your choice; here we use add_steps : # EMR steps to be executed by EMR cluster SPARK_TEST_STEPS = [{ 'Name': 'Run Spark', 'ActionOnFailure': 'CANCEL_AND_WAIT', 'HadoopJarStep': { 'Jar': 'command-runner.jar', 'Args': ['spark-submit', '/home/hadoop/aggregations.py', 's3://{}/data/transformed/green'.format(S3_BUCKET_NAME),

Management 82

Management Interactive Metadata Publishing 82

AWS Big Data

MARCH 3, 2023

You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. Amazon EMR Serverless is a serverless option in Amazon EMR that makes it easy for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers.

Data Lake 80

Data Lake Dashboards Metrics Metadata 80

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 102

Data Lake Management Metrics Data Warehouse 102

Rocket-Powered Data Science

MARCH 20, 2023

With all the data in and around the enterprise, users would say that they have a lot of information but need more insights to assist them in producing better and more informative content. This is where we dispel an old “big data” notion (heard a decade ago) that was expressed like this: “we need our data to run at the speed of business.”

Data Warehouse 324

Data Warehouse Metadata Digital Transformation Unstructured Data 324

AWS Big Data

MARCH 15, 2023

When designing the data processing pipeline for the attribute API, the Infomedia team wanted to use a flexible and open-source solution for processing data workloads with minimal operational overhead. The API retrieves data at runtime from an Amazon Aurora PostgreSQL-Compatible Edition database for end-user consumption.

Cost-Benefit 64

Cost-Benefit Data Processing Optimization Data-driven 64

AWS Big Data

JANUARY 27, 2023

With a unified data catalog, you can quickly search datasets and figure out data schema, data format, and location. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos. Refer to Catalogs for more information.

Data Lake 61

Data Lake Metadata Business Analysis Data-driven 61

AWS Big Data

AUGUST 1, 2023

For GlueDatabaseName , enter a unique name for the Data Catalog database to hold the Jira data table metadata (the default is jiralake ). This mode will scan all data and disable the change data capture (CDC) features of the stack. The DataBrew job performs data transformation and filtering tasks.

Data Lake 83

Data Lake Data Transformation Cost-Benefit Data-driven 83

IBM Big Data Hub

JULY 5, 2023

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data. Comprehensive data security and data governance (i.e.

Data Lake 68

Data Lake Metadata Cost-Benefit Data Warehouse 68

AWS Big Data

DECEMBER 21, 2023

To ingest the data, smava uses a set of popular third-party customer data platforms complemented by custom scripts. After the data lands in Amazon S3, smava uses the AWS Glue Data Catalog and crawlers to automatically catalog the available data, capture the metadata, and provide an interface that allows querying all data assets.

Data Lake 88

Data Lake Data Warehouse Data-driven B2B 88

Cloudera

NOVEMBER 22, 2021

In addition, more data is becoming available for processing / enrichment of existing and new use cases e.g., recently we have experienced a rapid growth in data collection at the edge and an increase in availability of frameworks for processing that data. As a result, alternative data integration technologies (e.g.,

Data Processing 89

Data Processing Data Warehouse Enterprise Visualization 89

Jet Global

MAY 1, 2023

Ideally, your primary data source should belong in this group. Modern Data Sources Painlessly connect with modern data such as streaming, search, big data, NoSQL, cloud, document-based sources. Quickly link all your data from Amazon Redshift, MongoDB, Hadoop, Snowflake, Apache Solr, Elasticsearch, Impala, and more.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52