Data Delivery

Seamlessly deliver processed data to BI, analytics, machine learning, and AI tools.

Get Data Where It Needs to Go, When It Needs To Be There, With the Ascend Data Automation Cloud

Native Notebook Connectors

Connect Jupyter, Zeppelin, and more directly to Ascend for fast and efficient access to data as it moves through your data workloads

Native BI & Data Visualization Access

Feed data directly from Ascend to your BI and Data Visualization tools via Ascend’s High Performance Records API

Multi-Point Data Delivery

With just a few clicks, replicate data to multiple endpoints—database, data warehouse, data lake or more
Ensure data integrity no matter how many delivery points with Ascend's DataAware intelligence
Save time and resources by optimizing cross-cloud data transfers

End-to-End Schema Management

Schemas are validated before the pipelines even run saving time, costs, and hassle
Never worry about schema mismatches at the delivery point again with Ascend's DataAware intelligence

				
					import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .master("local[*]") \
    .appName("test_sdl") \
    .config("spark.jars.packages", "org.apache.hadoop:hadoop-aws:3.2.0,com.amazonaws:aws-java-sdk-bundle:1.11.375") \
    .getOrCreate()

sc = spark.sparkContext

sc._jsc.hadoopConfiguration().set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
sc._jsc.hadoopConfiguration().set("fs.s3a.endpoint", "https://s3.ascend.io")
sc._jsc.hadoopConfiguration().set("fs.s3a.access.key", access_id)
sc._jsc.hadoopConfiguration().set("fs.s3a.secret.key", secret_key)

df = spark.read.parquet('s3a://trial/Getting_Started_with_Ascend/_DF__Clusters_w__Solar')

File-based Access

Get direct access to Ascend’s internal storage (.snappy.parquet) files for efficient processing by other big data systems
File-based Access offers fully transaction reads across multiple files, and guarantees that the data available is always linked directly to an active data pipeline

Record APIs and SDKs

Read records from any stage of any data pipeline via Ascend’s high throughput records API.
Connect Ascend directly to your applications, BI, and visualization tools with one easy API.

From Our Customers

"What I just did in an hour… would have taken me weeks. This is really cool. I don’t need to worry about data coming through the pipeline anymore. New data will show up and get pushed where it needs to go."

"Ascend's declarative engine is a game-changer for our team's productivity and its flex-code approach strikes the right balance, offering the flexibility we need—ranging from our simple SQL transforms to our most complex recursive PySpark transforms."

"The Ascend Platform is a phenomenal application for data engineering in companies like Be Power, which benefits from an end-to-end platform like Ascend instead of investing in custom open source solutions that take massive amounts of time and effort to build and operate. I’ve searched for something like Ascend for a long time.”

"We no longer look at individual data sources to pull into Ascend, we look at our entire enterprise—customer-facing and internal—and decide how to pull that into Ascend and then feed it into other systems for visualizations. Ascend is the gateway that processes all our data.”

Resources

The New Data Scale Challenge

From struggling with data volume and infrastructures to scaling data team capacity—what is the answer to increasing bandwidth?

Download Now

Whitepaper

DataAware Podcast

With a variety of guests from all facets of data engineering and associated teams, episodes look in-depth at the role of data engineering and data teams, trends, best (and worst) practices, real world use cases, and more.

Listen Now

PODCAST