Data
Engineering
Services

Extract. Transform. Load.
To let you experience the power of data.

In today's digital world, just having information is not enough. What's essential is the way you use your data. This is where data engineering comes into play. Data engineering takes control of collecting, sorting, and storing the required amounts of data. The thoughtfulness and meticulousness of this process allows businesses to have constant access to the data they need and it gives them the confidence that it's all up-to-date, correct, and reliable.

Data engineering service is an impactful tool that helps companies make decisions that increase brand recognition, enhance their reputation, minimize risks, and maximize revenue.

Turn your information into valuable insights that will drive your company's growth to reach new horizons and heights with a trusted Data Engineering company.

services

Data Engineering services we've provided to our clients

Data Scraping
Data Warehouse
Business Intelligence
Transaction Database
Stream Processing
Data Lake
Batch Processing
Big Data
Data Optimization
Data Automation
Data Consulting
ETL
Microservices oriented architectures
Real-time decision-making
Cloud Solutions

It's time!

When should you take
advantage of data
engineering services?

When you need to provide analysts and quality engineering teams with reliable data, it must be properly processed, handled, and presented in an accessible format.

When you have multiple data sources and want to organize them more clearly and conveniently, Solvd can assist you with transforming and integrating this data into a usable format.

When your data contains sensitive information about your business's inner processes and/or customers. In these cases, it's crucial to implement all of the necessary data security and compliance measures in order to prevent data leakage and disclosure.

We were most impressed with Solvd. Inc.'s holistic view of delivering a set of solutions.

Director of Connected Services, TuSimple

Joyce Tam

When the decision-making process has bottlenecks or lacks information due to poor data engineering processes. We allow businesses to overcome their information gaps by providing the appropriate tools and infrastructure to access, process, and analyze data.

When your massive amount of data can't be efficiently handled and used with legacy databases and tools. Data modernization is an effective way to seamlessly migrate away from an existing outdated system to a cloud storage infrastructure or new target platforms.

When your company spends a lot of resources on manual ETL processes, data engineering is a service that can automate these processes. This automation reduces both time wasted and human errors.

We
provide

Modernization of data

A multi-stage process of moving away from on-prem legacy systems towards advanced and more robust cloud-based storage. The result is actionable insights, improved decision-making processes, regulatory compliance, and increased efficiency.

Ingestion of data

Data ingestion lets teams stay ahead of the curve! The first step is extracting structured or unstructured raw data from various batch and stream processing sources. Next is data preparation, where this data is cleaned, refined, and converted into a format available for a particular cloud system or legacy database.

Real-time processing

Continuous input, ongoing processing, and consistent output enables enterprises to proactively find solutions, streamline operations, and personalize the user experience. Solvd implements batch and real-time processing systems in distributed environments based on cloud, web hosting, and mobile services.

Solutions for data-intensive apps

Data-intensive applications are typically built around several core functionalities and require fast and seamless data exchange. At Solvd, data engineers employ caches, search indexes, stream and batch processing, and other solutions to make data-intensive apps operate smoothly and efficiently.

Data CI/CD

Implementation of efficient production pipelines based on database objects, application data, infrastructure-as-code artifacts, as well as data validation and transformation logics, both in cloud-based and legacy deployment services. Data CI/CD practices are the key to faster time-to-insights, scalability, and reduced errors.

End-to-end data pipelines

End-to-end data pipelines are the backbone of data processing from the source to the final dashboards. It enables companies to save time, ensure consistency and standardization in data processing, and keep the workflow transparent. We build independent and replayable data workflow pipelines using various legacy, big data, cloud orchestration, and data management pipeline tools and techniques.

Data storage & ELT

The ELT series of operations simplifies and speeds up the process of entering, manipulating, and converting data into a usable format. It also helps businesses retain large volumes of information securely, and it also makes it accessible and searchable for future use. This storage can be in traditional relational databases, NoSQL databases, data lakes, or cloud-based storage solutions.

The data engineering process

Get the most at each stage

Gathering the requirements of data sources, types, and purposes. At this stage, we also determine the needs and at what delay the data is needed.

Designing the pipeline based on the requirements and building a plan to develop it with the correct ETA and analyzing the solution in order to optimize the cost of implementation.

Transformation of the data into a suitable format for storage and analysis. If needed, we extract the data into a raw layer that can help processes down the road get down to only one source. This can be known as Data Lake.

Building and implementing the storage solution that meets our client's needs. The next step is implementing the proper Data Monitoring tools.

Providing data analysts and data scientists with data access through data visualization and SQL queries. Solvd's team offers suggestions on the data that we find odd and suspicious.

Automation and maintenance of ongoing data collection, processing, and storage.

Process

What to expect when cooperating with Solvd

responsiveness. proficiency. courage.

Flexible operating models

Every business has its specific objectives, requirements, strategy, and resources. We understand that and, in turn, we offer various cooperation options. Staff augmentation, dedicated team, managed services, outsourcing, or consulting — choose the option that best fits your individual needs.

Smart solutions and thoughtful decisions

The more information you have, the more criteria you can consider when making a decision. Get limitless access to invaluable data that helps you build an insightful self-service analytic model in order to improve your decision-making process.

Valuable marketing and audience insights

The power of data engineering services enables businesses to uncover up-to-date information about the market, its tendencies, and its development direction. Deep analytics of this data lets them predict trends and swiftly respond to market dynamics. This all makes it easier to meet customers' needs.

Increased CTR and sales

Data-driven decisions, personalized marketing strategies, precise segmentation and targeting, real-time analytics, and an optimized customer journey lead companies to increased click-through rates, conversions, and sales.

Superior efficiency

Employing operational data and having access to distributor data helps companies improve supply chain visibility, illuminate gaps, and enhance replenishment efficiency.

Reduced time-to-market

By leveraging our frameworks and accelerators, you'll streamline data-related processes, improve collaboration, and speed up prototyping. This all results in a faster time-to-market without compromising on quality.

Our advanced
technology stack

Injest SaaS

Airbyte

Snowplow

Injest SaaS

Airbyte

Datacoral

Fivetran

Stitch

Azure

Data Factory

Snowplow

Rivery

Segment

Dataddo

Matillion

Compute

Databricks

Akka

Compute

Databricks

Spark

Cloudwick

Ray

Amazon EMR

Cloud Dataproc

Azure HDInsight

Akka

Trino

Dask

Ascend.io

Hadoop

Bodo.ai

Cloudera

Analytics engine

Pentaho

Starburst

Analytics engine

Pentaho

Cloudera Impala

Druid

Startree

Qubole

Firebolt

Synapse

Databend

Amazon

Redshift

Starburst

Snowflake

Dremio

ClickHouse

Rockset

Pinot

Imply

Amazon Athena

Google

BigQuery

Notebooks

Hex

Jupyter

Notebooks

Hex

Count

Noteable

Jupyter

Deepnote

Feature stores

Akka

Rasgo

Feature stores

Akka

Redis

Feast

Hopsworks

Scribble Data

Rasgo

Tecton

Kaskada

Vertex.ai

Objective storage

Alibaba Cloud

Purestorage

Objective storage

Alibaba Cloud

DigitalOcean

Filebase

IBM Cloud

Seagate

Zadara

Ceph

Hadoop

Oracle

Purestorage

SwiftStack

Lyve

Microsoft Azure

Wasabi

Cloud Storage

CloudFiles

Minio

Amazon S3

Inject teck

Spark

Amazon Kinesis

Inject teck

Spark

Google

Cloud Pub/Sub

Beam

Flink

StreamNative

Upsolver

Amazon Kinesis

Apache Storm

Tinybird

Kafka

Confluent

Beneath

Orchestration

Oozie

Flyte

Orchestration

Oozie

Prefect

Dagster

Astronomer

Flyte

Azure

Data Factory

Apache Airflow

Observability

Monte Carlo

Kensu

Observability

Monte Carlo

Datafold

Databand

Elementary

Redata

Acceldata

HoloClean

Whylabs

Lightup

Kensu

Great

expectations

Metaplane

Griffin

Timeseer.ai

Soda

Unravel

Bigeye

ML observability

Mana

Arthur

ML observability

Mana

Arize

Truera

Superwise

Whylabs

Gantry

Fiddler

Arthur

Aporia

Apres

Galileo

Deepchecks

Robust Intelligence

Analytics workflow

Dataform

Databricks

Analytics workflow

Dataform

Dbt

Databricks

Metastore

Hive

Databricks

Metastore

Hive

Cloud Dataproc

Azure Purview

Databricks

Cloudera

Amazon Glue

Git for data

Project Nessie

LakeFS

Git for data

Project Nessie

LakeFS

Open table formats

Apache ORC

Apache hudi

Open table formats

Apache ORC

Onehouse

Iceberg

Apache hudi

Delta Lake

Tabular

MLOps End-to-End

Clear ML

ZenML

MLOps End-to-End

Clear ML

Amazon

SageMaker

Data Iku

Verta

Mlflow

Neptune.ai

Cnvrg.io

Snorkel

Metaflow

Colab

Kubeflow

Valohai

Iguazio

ZenML

Comet

Hopsworks

Vertex.ai

Floydhub

OctoML

Domino

Google

Data Studio

Hydrosphere.io

Weights&Biases

Abacus.ai

Hugging Face

Discovery & Governance

Marquez

Boomi

Discovery & Governance

Marquez

Collibra

Open Metadata

Alation

Cloud Dataproc

Atlan

Boomi

BigID

Okera

Data.world

Immuta

Privacera

Industries

Be on the top everywhere!

Solvd covers a wide range of industries

Retail & E-commerce

Empower your strategy with out-of-the-box and invaluable data concerning today's market situation and your targeted audience's needs.

Financial Services

Data engineering is an essential part of fraud detection that enables financial companies to detect suspicious activity, prevent breaches, and save their reputation.

Software & Hi-Tech

Enable innovation, optimize and modernize operations, and enhance the research, development, and delivery process of your software products with Solvd.

Autonomous driving & Logistics

Obtain information from an unlimited number of resources and convert it into insights that will measurably improve workflow and reduce risk.

Media & Entertainment

Personalized marketing, real-time analytics, a recommendations engine, and more — all these advantages will make your service outstanding and sought-after.

Healthcare & Life Science

Well-established data assists with integrating disparate data sources and it also provides a comprehensive view of patient information, research data, and operational metrics.

Social Media

Rely on a deep analysis of your users' behavior, preferences, and expectations to make your social media platform even more competitive and profitable.

If your industry isn't above, don't worry.
There's no limit to us, so tell us your ideas.

The team was attentive, focused, and driven to deliver high-quality products according to timelines.

Head of Sales, Marketing & Customer Success, Everysight

Ilay Avni

OUR BLOG

Data Engineering insights

A Deep Dive into Key Industries Using AI

Six Key Industries Using AI

Read the article to learn about the six industries leading the AI revolution and how they use AI to drive innovation.

11 min

Leveraging Data for Strategic Insights and Competitive Advantage

Read the article to learn how to leverage data to gain strategic insights, develop a winning business strategy, and gain a competitive edge.

5 min

DataOps: The Secret Weapon for Data-Driven Decision-Making

Feeling overwhelmed by your data? DataOps can help. This guide explores what DataOps is, its benefits, and how it can transform your business.

4 min

Frequently asked
questions

FAQ

Is data engineering just ETL?

Data engineering is more of a broad-based approach than using ETL only; but at the same time, ETL is one of the essential components of data engineering. ETL stands for extract, transform, and load and its function is the process of extracting data from source systems, transforming it into a suitable format, and loading it into a target database or data warehouse. Meanwhile, data engineering also includes data integration, data quality assurance, data pipeline orchestration, and other activities.

What is data engineering in AWS?

The practice of designing, building, and managing data pipelines, processing, and storage solutions within the Amazon cloud environment is known as data engineering in AWS (Amazon Web Services). Businesses can successfully manage and use their data for various purposes, including analytics, machine learning, reporting, and more, by using the complete range of services and tools that AWS offers.

What is the difference between data engineering and data operations?

Data operations (DataOps) and data engineering are two activities that overlap in many ways as they focus on extracting, processing, and preparing data for further use. At the same time, they have a significant difference — it's the angle from which they approach this process.

What are the benefits of data engineering services?

It brings companies the following benefits:

Improved efficiency;

Automated data-related scope of tasks;

Data-driven decision-making processes;

Valuable insights about the market and the targeted audience;

Reduced time-to-market;

Better CTR, retention rate, and sales;

Real-time analytics;

Quicker response time;

The ability to keep up with the market, trends, and preferences.

Why is data engineering booming?

In today's fast-paced world, taking advantage of useful and rare information is a competitive advantage that drives businesses and industries toward their goals. Users share their data, interests, needs, and expectations, and companies need to be able to gather and use this information as much as possible and in the best way possible. Data engineering's role in this landscape is crucial, as it can massively help companies not only meet market needs but also transform internal data into optimized processes while reducing costs and increasing revenue.

What is data engineering and examples?

Data engineering is the process of collecting and using information in the most convenient and well-organized way possible. Every business has different amounts of information about its workflow, customers, processes, etc. Sometimes, the amount of data is not excessive and it can be processed and analyzed with the available programs and tools. However, when the amount of data is increasing in line with business development it leads to the necessity of integration of additional services such as data engineering.

How is SQL used in data engineering?

SQL stands for Structured Query Language, it's the main tool for managing data within a database. Data engineers use SQL and Python for ETL processes, data storage, data ingestion, data transformation, data cleaning and QA, migration, performance optimization, data warehousing, data reporting, data versioning, and auditing.

Data Engineering Services

Data Engineering services we've provided to our clients

When should you take advantage of data engineering services?

Modernization of data

Ingestion of data

Real-time processing

Solutions for data-intensive apps

Data CI/CD

End-to-end data pipelines

Data storage & ELT

The data engineering process

What to expect when cooperating with Solvd

Flexible operating models

Smart solutions and thoughtful decisions

Valuable marketing and audience insights

Increased CTR and sales

Superior efficiency

Reduced time-to-market

Our advanced technology stack

Solvd covers a wide range of industries

Retail & E-commerce

Financial Services

Software & Hi-Tech

Autonomous driving & Logistics

Media & Entertainment

Healthcare & Life Science

Social Media

Data Engineering insights

Six Key Industries Using AI

Leveraging Data for Strategic Insights and Competitive Advantage

DataOps: The Secret Weapon for Data-Driven Decision-Making

Frequently asked questions

Is data engineering just ETL?

What is data engineering in AWS?

What is the difference between data engineering and data operations?

What are the benefits of data engineering services?

Why is data engineering booming?

What is data engineering and examples?

How is SQL used in data engineering?

Let's Talk

Data
Engineering
Services

When should you take
advantage of data
engineering services?

Our advanced
technology stack

Frequently asked
questions

Let's
Talk