Ascend 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
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')
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.”
The New Data Scale Challenge
From struggling with data volume and infrastructures to scaling data team capacity—what is the answer to increasing bandwidth?
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.
A Deep Dive Into Data Orchestration at Harry's
Learn how the Harry's data science team expedited ingesting, transforming, and delivering retail data feeds into a new, robust shared data model.